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type="application/json">{"props":{"pageProps":{"page":{"metadata":{"tags":[],"concepts":[]},"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"pzdvlnh8omga"}},"id":"51FUqR3lqqvp43MNR8r2C1","type":"Entry","createdAt":"2022-04-05T23:32:40.561Z","updatedAt":"2024-12-17T22:00:50.910Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"publishedVersion":62,"revision":18,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"archivePage"}},"locale":"en-US"},"fields":{"title":"Blog Archive","slug":"blog","metaTitle":"Blog | Dotmatics","metaDescription":"Discover the latest trends and updates in the science realm.","mainSectionContent":{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Scientific Research Blog\r\n\r","nodeType":"text"}],"nodeType":"heading-1"},{"data":{},"content":[{"data":{},"marks":[],"value":"Discover the latest insights and perspectives to lead your research and 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Posts","featuredPosts":[{"metadata":{"tags":[],"concepts":[]},"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"pzdvlnh8omga"}},"id":"6joxOsTqi4Td7USzOrlCgH","type":"Entry","createdAt":"2024-09-11T13:46:05.403Z","updatedAt":"2024-09-16T16:38:09.982Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"publishedVersion":109,"revision":7,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"postPage"}},"locale":"en-US"},"fields":{"title":"How to Plan Your AI Budget Now To Succeed in 2025 ","slug":"how-to-plan-your-ai-budget-now-to-succeed-in-2025","date":"2024-09-13T00:00+00:00","showPostNavigation":false,"content":{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"In 2025 AI will be at the center of business strategy with huge investments, especially in life sciences. Over time, it will reduce the cycle length and failure rate in scientific research and critical drug discovery areas, thus significantly lower the dollars per drug approved. ","nodeType":"text"},{"data":{"uri":"https://www.towardshealthcare.com/insights/ai-in-life-sciences-market"},"content":[{"data":{},"marks":[],"value":"One report shows","nodeType":"text"}],"nodeType":"hyperlink"},{"data":{},"marks":[],"value":" [1] that the life sciences market size for AI investment is expected to reach nearly $10 billion before 2032. ","nodeType":"text"},{"data":{"uri":"https://www.bain.com/insights/how-to-successfully-scale-generative-ai-in-pharma/"},"content":[{"data":{},"marks":[],"value":"Bain and Company reports","nodeType":"text"}],"nodeType":"hyperlink"},{"data":{},"marks":[],"value":" [2] that 40 percent of pharma companies are including anticipated savings from generative artificial intelligence in their 2024 budgets. ","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"At the same time, growing costs is one of the primary threats to its success. GenAI specifically is rapidly being integrated into the life science value chain. While GenAI is just part of AI/ML that is being completed by life science organizations, it is certainly one of the major areas of interest, and is an area where costs and ROI are still largely unknown.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"","nodeType":"text"},{"data":{"uri":"https://www.gartner.com/document-reader/document/5638991?ref=sendres_email\u0026refval=79002868"},"content":[{"data":{},"marks":[],"value":"Gartner surveyed life science executives","nodeType":"text"}],"nodeType":"hyperlink"},{"data":{},"marks":[],"value":" [3] in June 2024 to assess sentiment and activity. Seventy-two percent of respondents have at least one GenAI use case in production and 30 percent are deploying six or more; 92 percent have at least one use case currently in pilot. However, Gartner reports that more than half of organizations abandon their efforts due to cost-related missteps. ","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Because the AI space is changing rapidly, it’s essential that companies are budgeting correctly and using their investments strategically. Here’s how to plan your AI budget to be successful in the year ahead.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[{"type":"bold"}],"value":"Spend money to help scientists increase the value of data","nodeType":"text"}],"nodeType":"heading-3"},{"data":{},"content":[{"data":{},"marks":[],"value":"From elementary science classes to graduate work studies, the ‘Make-Test-Decide’ cycle is taught and understood logically. Today, the injection of AI and scientific intelligence platforms are speeding up this Lab-in-a-Loop concept – a beautiful interplay between the scientific wet lab where experiments are physically performed, and the dry lab, where experiments can be simulated and modeled and where informed AI engines can recommend the next experiment to test in the wet lab. ","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"First and foremost when it comes to AI, investments should make the life of scientists easier by supporting that Lab-in-a-Loop lifecycle, which means: ","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[{"type":"bold"}],"value":"Scientifically-smart technologies - ","nodeType":"text"},{"data":{},"marks":[],"value":"Scientists know what data is needed to move faster; they simply need access. Science-first tools can also help companies reduce cost by only capturing relevant data. PhDs weren’t earned for pivot tables. ","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[{"type":"bold"}],"value":"FAIR data \u0026 FAIR processes for reproducibility - ","nodeType":"text"},{"data":{},"marks":[],"value":"Enable scientists to gather, understand, use, and perform data driven work as they want to without creating a heavy burden of manual metadata tagging. Research is unknown by definition. FAIR data and processes across locations, departments, modalities, and experiments provide transparency to previously invisible possibilities. ","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[{"type":"bold"}],"value":"Future-proof technologies - ","nodeType":"text"},{"data":{},"marks":[],"value":"Employ tools that will grow and scale with your organization and industry, enabling flexibility not just as targets and focus change, but also offering the ability to seamlessly connect various departments and modalities. Don’t let legacy software constraints limit how people should collaborate with one another. ","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[{"type":"bold"}],"value":"Low-code or no-code technologies - ","nodeType":"text"},{"data":{},"marks":[],"value":"Introduce resources that put the power in scientists hands to decrease the burden on data science and IT, and significantly expand the value of AI / ML. ","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"}],"nodeType":"unordered-list"},{"data":{},"content":[{"data":{},"marks":[{"type":"bold"}],"value":"Put your AI dollars where they matter most ","nodeType":"text"}],"nodeType":"heading-3"},{"data":{},"content":[{"data":{},"marks":[],"value":"We know most companies are spending money on AI — but are you spending it efficiently? Companies need a spend strategy or else costs will escalate without control. At its core, there are a few large drivers of cost when we think about AI, which include data, compute, and people (i.e the scientists). You can’t simply dump data in a data lake. “Garbage in, garbage out” is a tale as old as time. GenAI can’t deliver expected results unless a proper data architecture and infrastructure is in place. You want to maximize storage, compute, security, GPUs, training, and storage costs. That means seeking out smart, integrated data strategies that enhance an organization's existing software spend. ","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Data in itself is a large cost model. And it’s often the most volatile because of quality, availability, and governance around it due to the number of data sources and amount of data within each of those sources. While acquiring some of that data could be free, such as from public sources, creating experiments that utilize a company's proprietary data is often prohibitively expensive. These data come from years of experiments, experience, and knowledge created in the discovery process, and it’s what separates a company from their competition. More data doesn’t always translate to a better model. However, using it effectively at scale is a costly virtuous cycle. Companies want to perform work from wet labs to create more data, and then bring in public data sources, which in turn will generate more data, to then plug those data back into models, to get better models, and so on and so on. We talk frequently about a Lab-in-a-Loop; think of this as “Data-in-a-Loop”. The cost to support such a process is immensely expensive. ","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Plus, cost consideration is not limited to buying, creating, and modeling—maintenance is a major factor. Just because you buy data doesn’t mean it's easy to model, cleanse, and clean. You must label and follow the ontology structure to align with what you’re modeling. The easier it is to add more data into the model, the better the outcomes. That’s a time-consuming and complex task that requires teams to understand how models get trained, and how they can be easily used with more experiments and data to create more data. Add in instrumentation challenges, combined with the general challenges and cost of maintaining on-premise or client-cloud solutions, and the result is significant IT overhead costs and time to manage, creating drastic increases in the overall time and costs of R\u0026D.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"It’s no surprise that ","nodeType":"text"},{"data":{"uri":"https://www.gartner.com/document-reader/document/5638991?ref=sendres_email\u0026refval=79002868"},"content":[{"data":{},"marks":[],"value":"that same Gartner report","nodeType":"text"}],"nodeType":"hyperlink"},{"data":{},"marks":[],"value":" [4] shows that a group of early adopters is distinctly outpacing the rest of organizations in life sciences. Nearly 15 percent of respondents have already deployed 11 or more use cases in production, with an additional 15 percent having deployed between six and 10 use cases. These leaders are actively defining the pathways for AI application for accelerating drug discovery.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Ultimately, leading organizations will establish budgeting and governing models that prioritize high-return use cases. Those use cases will align with their investment themes around big bets for the future of the business. In addition, organizations often bill AI investments to IT, although they typically deliver cost-benefits to the respective functional budgets. Don’t let these conflicts hinder adoption; find ways to incentivize business unit leaders to invest in disruptive, value-generating AI initiatives.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[{"type":"bold"}],"value":"Tips to setting your AI budget effectively ","nodeType":"text"}],"nodeType":"heading-3"},{"data":{},"content":[{"data":{},"marks":[],"value":"At the highest level, use your AI budget on science, versus operational efficiency or other AI use cases. Some other best practices include:","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"To enable efficient analysis of the cost of AI products, it's important to ","nodeType":"text"},{"data":{},"marks":[{"type":"bold"}],"value":"understand pricing metric definitions and how pricing structures scale with use","nodeType":"text"},{"data":{},"marks":[],"value":". There are significant differences between vendors’ offerings, with some being priced by tokens and others by character counts, for example. ","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[{"type":"bold"}],"value":"Use a standard means of assessing AI vendors’ proposals ","nodeType":"text"},{"data":{},"marks":[],"value":"that models annualized costs-as pricing approaches vary. For AI products, ensure all costs are modeled, including fine-tuning, configuration and retraining. ","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[{"type":"bold"}],"value":"Minimize unbudgeted costs ","nodeType":"text"},{"data":{},"marks":[],"value":"by negotiating scalability for growth and requiring transparency about hidden costs, such as “user+” models with unplanned overage of prepaid credits.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[{"type":"bold"}],"value":"Identify and integrate key metrics early in the AI ideation phase. ","nodeType":"text"},{"data":{},"marks":[],"value":"Ensure that post-deployment metrics effectively measure impact and calculate ROI. ","nodeType":"text"},{"data":{"uri":"https://www.gartner.com/document-reader/document/5638991?ref=sendres_email\u0026refval=79002868"},"content":[{"data":{},"marks":[],"value":"From that Gartner survey","nodeType":"text"}],"nodeType":"hyperlink"},{"data":{},"marks":[],"value":",[5] 29 percent of life sciences leaders said that determining the tangible outcomes and financial impact of AI implementations is a top challenge, complicating investment decisions. ","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[{"type":"bold"}],"value":"And perhaps most importantly, consider cost savings. ","nodeType":"text"},{"data":{},"marks":[],"value":"New AI tech is not only an additive cost—you can replace technologies and cut costs to open up budget. Focusing on how to free up scientists' time should always be front of mind. Prioritizing initial use cases for assistive AI, which allow for methods setup, protocol templates, automatically generated reports, and other repetitive work to be executed at scale, will help to remove “admin” costs while also reducing errors and better align transactional work with transactional costs. Freeing up time will allow for an increased opportunity to train and better leverage expertise to support that Data-in-a-Loop workflow. The end goal is for scientists, who know science, to help early and often to train and improve models.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"}],"nodeType":"unordered-list"},{"data":{},"content":[{"data":{},"marks":[{"type":"bold"}],"value":"Scientific lens on people, data, and how data goes into scientific models","nodeType":"text"}],"nodeType":"heading-3"},{"data":{},"content":[{"data":{},"marks":[],"value":"In such a rapidly emerging area it’s important to get creative and think out of the box. Within the next five years—not the next 50 years!—the cost and timeline of drug discovery will drastically change. As the landscape and complexity of AI within life sciences continue to evolve, CIOs must be mindful of their investments ","nodeType":"text"},{"data":{},"marks":[{"type":"italic"}],"value":"and ","nodeType":"text"},{"data":{},"marks":[],"value":"their ability to safely and effectively implement and scale these technologies into their existing landscapes moving forward.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"As exciting as the promise of AI is, we must remember that organizations are built on people. While new technology can be predictive and automated, we must ensure that scientists aren’t overburdened. That means employing software that enables FAIR data, FAIR processes, and ensuring that the cost of science doesn’t get cut to fund the cost of technology. Tremendous efficiencies and scientific advancement are possible, but ultimately the role of the scientist must be at the forefront in the development of any policies or technologies on your AI journey. 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(Image credit: Weidle, U.H, Tiefenthaler, G., Weiss, G., et al. Cancer Genomics \u0026 Proteomics. 2013, Jan, 10 (1): 1-18.)","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Three Must-Dos for Multiformat Antibody Design","nodeType":"text"}],"nodeType":"heading-2"},{"data":{},"content":[{"data":{},"marks":[],"value":"As antibody formats get more complex, so does the process of tracking them. Take, for example, multispecific antibodies. These structures are often quite large and are connected in complicated ways, sometimes with multiple binding domains. Teams frequently mix and match different parts to explore the impact of small design changes. (Think of it as a “Frankenstein” approach to antibody design!) As teams iterate through these design changes and study their effects, they must thoroughly track an incredible amount of interconnected data, which is often easier said than done. 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Overview of bispecific antibody formats reduced to practice, grouped into molecules with symmetric or asymmetric architecture.\n(Image credit: Brinkmann and Kontermann. MAbs. 2017 Feb-Mar; 9(2): 182–212) [4]","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"1. Manage Moving Pieces","nodeType":"text"}],"nodeType":"heading-3"},{"data":{},"content":[{"data":{},"marks":[],"value":"Sequences. Variable regions. Linkers. Conjugates. All the various pieces of multiformat antibodies—and details regarding how they come together as a whole—need to be recorded and managed. Teams need to capture antibody design, assign unique identifiers to all components, record origin details, track modifications, analyze assembly, and assess activity. Unfortunately, many teams are still trying to manage all these moving parts with workaround solutions like massive spreadsheets. This quickly becomes unsustainable as researchers mix and match complex components through several design iterations, trying to find that magic combination. ","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"2. Speak the Same Language","nodeType":"text"}],"nodeType":"heading-3"},{"data":{},"content":[{"data":{},"marks":[],"value":"With so many moving parts at play, teams need to speak the same language in order to track projects and collaborate efficiently. As antibody structures become more complex, so does the task of naming them. Many teams struggle to establish or adopt consistency in naming conventions because their informatics systems don't offer an easy way to do so. But standardizing ontology is imperative. Teams need consistent ways to describe the antibody format type (e.g., bispecific or multispecific), record the source, identify the intended target, clarify the location and type of conjugates or radioisotopes, describe chemical modification, track product or project details, etc. Consistently capturing these details best positions teams to collaboratively design novel therapeutics; it is also a necessary precursor to machine-readiness. Ultimately, updating informatics solutions to use high-level code to describe structures in a way that both humans and computers can easily understand will be necessary for leveraging computer-aided design for AI antibody discovery.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"3. 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multidisciplinary teams, scientists need to optimize their data flow so they can focus on collaborative discovery and innovation. They need a better way to share data and build off their collective knowledge. ","marks":[],"data":{}}]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"LifeArc, a UK-based medical charity, has sought to break down barriers between disciplines so their research teams can collectively make critical, data-driven decisions and ultimately identify and pursue the most promising design ideas.","marks":[],"data":{}}]},{"nodeType":"heading-2","data":{},"content":[{"nodeType":"text","value":"Case Study: Streamlined Biologics and Small Molecule Drug Discovery and Design LifeArc","marks":[],"data":{}}]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"LifeArc has been on a mission to “make life science life changing” for more than two decades. The UK-based medical charity is transforming the way life science ideas are brought to fruition by bridging the gap between lab and patient. Their novel approach has already led to four licensed medications, with several more in trials.","marks":[],"data":{}}]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"LifeArc’s drug discovery and design processes are remarkably diverse and collaborative on all fronts. ","marks":[],"data":{}}]},{"nodeType":"unordered-list","data":{},"content":[{"nodeType":"list-item","data":{},"content":[{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"Scientists","marks":[{"type":"bold"}],"data":{}},{"nodeType":"text","value":": The company has over 100 internal R\u0026D scientists whose exploration of drugs and targets is informed by feedback from off-site molecular diagnostic colleagues.","marks":[],"data":{}}]}]},{"nodeType":"list-item","data":{},"content":[{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"Partners","marks":[{"type":"bold"}],"data":{}},{"nodeType":"text","value":": In addition to internal researchers, LifeArc has a ","marks":[],"data":{}},{"nodeType":"hyperlink","data":{"uri":"https://www.dotmatics.com/solutions/collaborative-research-network-software"},"content":[{"nodeType":"text","value":"large network of pharma and biotech partners","marks":[],"data":{}}]},{"nodeType":"text","value":", academic collaborators, and contract research organizations (CROs) who are all producing important data that must flow into the company’s data warehouse.","marks":[],"data":{}}]}]},{"nodeType":"list-item","data":{},"content":[{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"Modalities","marks":[{"type":"bold"}],"data":{}},{"nodeType":"text","value":": LifeArc is working with both small molecules and biologics, including engineered antibodies such as antibody-drug conjugates – which means many","marks":[],"data":{}},{"nodeType":"hyperlink","data":{"uri":"https://www.dotmatics.com/blog/chemically-modified-biologics-data"},"content":[{"nodeType":"text","value":" diverse chemical and biological data types","marks":[{"type":"underline"}],"data":{}}]},{"nodeType":"text","value":" must be accommodated.","marks":[],"data":{}}]}]},{"nodeType":"list-item","data":{},"content":[{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"Diseases","marks":[{"type":"bold"}],"data":{}},{"nodeType":"text","value":": The company is exploring therapeutics for a wide range of conditions, including cancer, Crohn’s disease, multiple sclerosis, rheumatoid arthritis, and COVID-19.","marks":[],"data":{}}]}]},{"nodeType":"list-item","data":{},"content":[{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"Informatics needs","marks":[{"type":"bold"}],"data":{}},{"nodeType":"text","value":": LifeArc’s informatics systems must handle ","marks":[],"data":{}},{"nodeType":"hyperlink","data":{"uri":"https://www.dotmatics.com/solutions/data-intelligence-lab-software"},"content":[{"nodeType":"text","value":"huge data volumes","marks":[],"data":{}}]},{"nodeType":"text","value":", both internal and external data sources, and highly variable data types, including:","marks":[],"data":{}}]},{"nodeType":"unordered-list","data":{},"content":[{"nodeType":"list-item","data":{},"content":[{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"Small molecule discovery data (e.g., public and proprietary compound databases, CRO assay data, property calculations)","marks":[],"data":{}}]}]},{"nodeType":"list-item","data":{},"content":[{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"Biologics R\u0026D data (e.g., B-cell workflows, phage display, immunization, humanization, X-ray crystallography)","marks":[],"data":{}}]}]},{"nodeType":"list-item","data":{},"content":[{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"Associated data and metadata (e.g., patent data, predicted data, ontologies, annotations, batch data, purification and expression data)\n","marks":[],"data":{}}]}]}]}]}]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"With such diversity at play, LifeArc’s ability to fulfill its mission hinges upon seamless collaboration and data flow between its multidisciplinary R\u0026D teams and their many partners across pharma, biotech, and academia. This level of interconnectivity demands an end-to-end data platform like Dotmatics, which removes barriers between teams, breaks down data silos, and lets researchers easily access and build off their collective R\u0026D data. ","marks":[],"data":{}}]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"“With such an eclectic mix of therapeutic areas and modalities, LifeArc needed an informatics platform that was fit for purpose. We believe that the Dotmatics platform gives us the flexibility to plug in and play any other tools that we have now or might need in the future,” explained one LifeArc scientist.","marks":[],"data":{}}]},{"nodeType":"heading-2","data":{},"content":[{"nodeType":"text","value":"Generate Better Insights With Improved R\u0026D Data Processes ","marks":[],"data":{}}]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"With Dotmatics solutions, LifeArc’s people and processes work together, better than ever. Some of the Dotmatics solutions that LifeArc has implemented include:","marks":[],"data":{}}]},{"nodeType":"unordered-list","data":{},"content":[{"nodeType":"list-item","data":{},"content":[{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"","marks":[],"data":{}},{"nodeType":"hyperlink","data":{"uri":"https://www.dotmatics.com/platform"},"content":[{"nodeType":"text","value":" ","marks":[],"data":{}},{"nodeType":"text","value":"R\u0026D Data Platform","marks":[{"type":"underline"},{"type":"bold"}],"data":{}}]},{"nodeType":"text","value":" with ","marks":[],"data":{}},{"nodeType":"hyperlink","data":{"uri":"https://www.dotmatics.com/capabilities/scientific-data-search"},"content":[{"nodeType":"text","value":"Scientific Search","marks":[{"type":"underline"},{"type":"bold"}],"data":{}}]},{"nodeType":"text","value":":","marks":[{"type":"bold"}],"data":{}},{"nodeType":"text","value":" Single search across all diverse data sources.","marks":[],"data":{}}]}]},{"nodeType":"list-item","data":{},"content":[{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"","marks":[],"data":{}},{"nodeType":"hyperlink","data":{"uri":"https://www.dotmatics.com/capabilities/visualization-and-analytics"},"content":[{"nodeType":"text","value":"Data Visualization and Analysis","marks":[{"type":"underline"},{"type":"bold"}],"data":{}}]},{"nodeType":"text","value":": Project-based viewing of all relevant data, which can be further analyzed and modeled to make data-driven R\u0026D decisions.","marks":[],"data":{}}]}]},{"nodeType":"list-item","data":{},"content":[{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"","marks":[],"data":{}},{"nodeType":"hyperlink","data":{"uri":"https://www.dotmatics.com/solutions/small-molecule-discovery"},"content":[{"nodeType":"text","value":"Compound Design Tools","marks":[{"type":"underline"},{"type":"bold"}],"data":{}}]},{"nodeType":"text","value":": Compound design informed by diverse data derived from structural drawings, similarity searching, property prediction, modeling, and machine learning.","marks":[],"data":{}}]}]},{"nodeType":"list-item","data":{},"content":[{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"","marks":[],"data":{}},{"nodeType":"hyperlink","data":{"uri":"https://www.dotmatics.com/capabilities/entity-registration"},"content":[{"nodeType":"text","value":"Biological and Chemical Entity Registration","marks":[{"type":"underline"},{"type":"bold"}],"data":{}}]},{"nodeType":"text","value":"- Registration for both small molecule drugs and biologics, including DNA, RNA, peptides, proteins, antibodies, conjugates, non-natural peptides and nucleotides, plasmids, cell lines, and user-defined entities.","marks":[],"data":{}}]}]}]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"Dotmatics solutions have had a huge impact on LifeArc researchers’ day-to-day work. One expert explains, “All data are available for scientists to use and easy to locate, which saves a lot of time. For example, it is possible to see if a compound has a SureChEMBL patent, without the need to look up elsewhere. Our growing team of data scientists can also reuse the data for predictive models, machine learning and AI.”","marks":[],"data":{}}]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"Read the ","marks":[],"data":{}},{"nodeType":"hyperlink","data":{"uri":"https://www.dotmatics.com/case-studies/leveraging-informatics-and-modeling-tools-to-develop-a-comprehensive-design"},"content":[{"nodeType":"text","value":"full LifeArc case study","marks":[],"data":{}}]},{"nodeType":"text","value":" about leveraging informatics and modeling tools to develop a comprehensive Design Make and Test platform.","marks":[],"data":{}}]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"","marks":[],"data":{}}]},{"nodeType":"heading-2","data":{},"content":[{"nodeType":"text","value":"Multimodal Therapeutic Discovery on a Unified R\u0026D Platform","marks":[],"data":{}}]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"Dotmatics solutions break down barriers between multidisciplinary teams, helping them better collaborate, share data, and build off their collective knowledge. With data flows and workflows optimized, researchers can return their focus to innovation and idea generation. Dotmatics solutions can:","marks":[],"data":{}}]},{"nodeType":"unordered-list","data":{},"content":[{"nodeType":"list-item","data":{},"content":[{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"Provide a connected, ","marks":[],"data":{}},{"nodeType":"hyperlink","data":{"uri":"https://www.dotmatics.com/platform"},"content":[{"nodeType":"text","value":"end-to-end research platform","marks":[{"type":"underline"}],"data":{}}]},{"nodeType":"text","value":" for both ","marks":[],"data":{}},{"nodeType":"hyperlink","data":{"uri":"https://www.dotmatics.com/solutions/small-molecule-discovery"},"content":[{"nodeType":"text","value":"small molecule drug discovery","marks":[{"type":"underline"}],"data":{}}]},{"nodeType":"text","value":" and ","marks":[],"data":{}},{"nodeType":"hyperlink","data":{"uri":"https://www.dotmatics.com/solutions/biologics-discovery"},"content":[{"nodeType":"text","value":" biologics research and development","marks":[{"type":"underline"}],"data":{}}]},{"nodeType":"text","value":"","marks":[],"data":{}}]}]},{"nodeType":"list-item","data":{},"content":[{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"Combine biology and chemistry data and use it to guide decision making","marks":[],"data":{}}]}]},{"nodeType":"list-item","data":{},"content":[{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"Facilitate cross-discipline research and promote workflow optimization","marks":[],"data":{}}]}]},{"nodeType":"list-item","data":{},"content":[{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"Simplify database and technology infrastructure and reduce total cost of ownership","marks":[],"data":{}}]}]}]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"Request a product demonstration to discuss how Dotmatics can help improve your data flow for accelerated and advanced insights.","marks":[],"data":{}}]},{"nodeType":"embedded-entry-block","data":{"target":{"metadata":{"tags":[],"concepts":[]},"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"pzdvlnh8omga"}},"id":"aclcWLTGM1eHAmqQlvwkj","type":"Entry","createdAt":"2022-10-28T14:44:01.178Z","updatedAt":"2023-03-23T21:06:15.459Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"publishedVersion":43,"revision":11,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"html"}},"locale":"en-US"},"fields":{"title":"Personalized Demo CTA Button","html":"\u003ca class=\"cta-forest cta\" href=\"/demo-request\"\u003eRequest A Personalized Demo\u003c/a\u003e"}}},"content":[]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"","marks":[],"data":{}}]}]},"thumbnail":{"metadata":{"tags":[],"concepts":[]},"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"pzdvlnh8omga"}},"id":"6aMxyIdKpMSWzLiTv6nMYZ","type":"Asset","createdAt":"2023-04-06T18:45:32.190Z","updatedAt":"2023-04-06T18:45:32.190Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"publishedVersion":7,"revision":1,"locale":"en-US"},"fields":{"title":"multimodal research data flow","description":"","file":{"url":"//images.ctfassets.net/pzdvlnh8omga/6aMxyIdKpMSWzLiTv6nMYZ/47d99a0229037f38aad1a946a296a8cf/research_lab_.jpg","details":{"size":3861111,"image":{"width":5120,"height":2880}},"fileName":"research lab .jpg","contentType":"image/jpeg"}}},"timeToRead":"9 min","theme":"Grey","metaTitle":"Optimizing Multimodal R\u0026D by Improving Data Flow","metaDescription":"Learn how connecting your data sources can improve data flow and ultimately accelerate and advance your multimodal research."}},{"metadata":{"tags":[],"concepts":[]},"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"pzdvlnh8omga"}},"id":"7r6hWCKYFeNcYr1TOH9Wbi","type":"Entry","createdAt":"2023-05-03T21:11:49.618Z","updatedAt":"2023-07-14T16:34:51.973Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"publishedVersion":74,"revision":4,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"postPage"}},"locale":"en-US"},"fields":{"title":"Integrated Multi-Omic Data: Powering Precision Medicine","slug":"integrated-multi-omic-data-powering-precision-medicine","date":"2023-05-04T00:00-12:00","showPostNavigation":true,"content":{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"As we discussed in our previous blog, “","nodeType":"text"},{"data":{"uri":"https://www.dotmatics.com/blog/transitioning-to-precision-medicine"},"content":[{"data":{},"marks":[{"type":"underline"}],"value":"Transitioning To Precision Medicine","nodeType":"text"}],"nodeType":"hyperlink"},{"data":{},"marks":[],"value":",” precision medicine is poised to transform medicine as we’ve known it, from the earliest R\u0026D efforts by drug developers all the way to patient assessment and physician prescribing. In fact, it is projected that the precision medicine market will reach into the hundreds of billions of dollars in the coming years.(1) But getting there presents some of the most challenging data science and interdisciplinary collaboration demands we’ve ever seen.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Unprecedented Data Science and Collaboration Demands","nodeType":"text"}],"nodeType":"heading-2"},{"data":{},"content":[{"data":{},"marks":[],"value":"While patient and prescriber may first come to mind when thinking about precision medicine, the diversity of people working toward this progressive approach to medicine is really quite stunning. For example:","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Life science researchers are uncovering disease mechanisms and identifying diagnostic and prognostic biomarkers that can help assess individual patient needs, as well as guide clinical drug development.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Genomics companies are making sequencing data more accessible by lowering costs and increasing throughput, while pharmacogenomics experts are deciphering how those data relate to the way patients respond to drugs, and other “omics” researchers are exploring the impact of additional biological processes beyond the genome.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Imaging and AI tools are aiding in the analysis of patient’s medical scans with accuracy and speed not possible through manual assessment.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Diagnostics and medtech companies are creating tests, equipment, and wearable devices to provide real-time metrics on patients and their environments.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Informatics and electronics-health-records (ELH) companies are creating solutions to collect, combine, and securely manage multimodal patient data.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Regulators, policymakers, and insurers are navigating red-tape and incentivizing change.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"}],"nodeType":"unordered-list"},{"data":{},"content":[{"data":{},"marks":[],"value":"Truly Integrated Biomedical Data","nodeType":"text"}],"nodeType":"heading-2"},{"data":{},"content":[{"data":{},"marks":[],"value":"With so many contributing elements, large volumes of diverse data comprise patients’ ever-evolving health profiles. The figure below was created by researchers at Yale School of Medicine, Harvard Medical School, and Scripps Research Translational Institute to illustrate the complex interplay between the diverse biomedical data modalities available today and the many opportunities for putting the data to use.(2)","nodeType":"text"}],"nodeType":"paragraph"},{"data":{"target":{"metadata":{"tags":[],"concepts":[]},"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"pzdvlnh8omga"}},"id":"5SdVfpRnCIL68x7KxmiDeJ","type":"Asset","createdAt":"2023-05-03T21:02:52.124Z","updatedAt":"2023-05-03T21:02:52.124Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"publishedVersion":5,"revision":1,"locale":"en-US"},"fields":{"title":"integrated precision medicine","description":"","file":{"url":"//images.ctfassets.net/pzdvlnh8omga/5SdVfpRnCIL68x7KxmiDeJ/920f3360a19278c324c075e08364f63e/integrated_precision_medicine.png","details":{"size":1233777,"image":{"width":1600,"height":1242}},"fileName":"integrated precision medicine.png","contentType":"image/png"}}}},"content":[],"nodeType":"embedded-asset-block"},{"data":{},"content":[{"data":{},"marks":[],"value":"Figure 1: Biomedical data modalities and opportunities for their application.\n (Acosta, J.N., Falcone, G.J., Rajpurkar, P. and Topoal, ","nodeType":"text"},{"data":{},"marks":[{"type":"underline"}],"value":"E.J. Nature Medicine","nodeType":"text"},{"data":{},"marks":[],"value":" [28] 2022.)","nodeType":"text"}],"nodeType":"heading-6"},{"data":{},"content":[{"data":{},"marks":[],"value":"","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"All these data points fit together like pieces of a puzzle. Except these data aren’t stagnant, and biological context dictates everything, so the puzzle pieces can shift. Therefore, attaining a clear and actionable picture is an iterative and recursive process. This is a daunting ask, even for companies with strong data infrastructures and data science expertise. Unfortunately, many healthcare organizations feel ill-prepared to take on precision medicine programs for a variety of reasons, from data-science preparedness to IT readiness to skills gaps in analytics, programming and specialized science.(3) In fact, many companies are already struggling to achieve efficiency under the current paradigm and a shift toward personalized medicine will only increase the need to adopt advanced data technologies and fully leverage the data at hand.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Multi-Omics Data","nodeType":"text"}],"nodeType":"heading-2"},{"data":{},"content":[{"data":{},"marks":[],"value":"To demonstrate the scope of the data challenges presented by precision medicine, let’s dive deeper into just one of the many data modalities noted in Figure 1—omics data. As illustrated in Figure 2, multi-omics data are the collective data from different “omics'' research areas, such as genomics, epigenomics, transcriptomics, proteomics, metabolomics and phenomics. These data originate from many different assay types and experiments and span many different scales of time and space. And while genomics has long been a primary focus in precision medicine, other areas of research are increasingly being used to help attain a more complete picture of how an individual’s complex biology impacts their health profile. ","nodeType":"text"}],"nodeType":"paragraph"},{"data":{"target":{"metadata":{"tags":[],"concepts":[]},"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"pzdvlnh8omga"}},"id":"1iTEXRf0uWiFZU97Rc9s8O","type":"Asset","createdAt":"2023-05-03T21:01:41.881Z","updatedAt":"2023-05-03T21:01:41.881Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"publishedVersion":5,"revision":1,"locale":"en-US"},"fields":{"title":"mult-omics data","description":"","file":{"url":"//images.ctfassets.net/pzdvlnh8omga/1iTEXRf0uWiFZU97Rc9s8O/6ec5e522a1d773ece4002e329d51760a/multiomics_data.png","details":{"size":563372,"image":{"width":1184,"height":941}},"fileName":"multiomics data.png","contentType":"image/png"}}}},"content":[],"nodeType":"embedded-asset-block"},{"data":{},"content":[{"data":{},"marks":[],"value":"Figure 2: Multi-omics data can help provide a more complete picture of how an individual’s complex biology impacts their health profile. (Kim et. al, Mol. Plant. [9] 2016)","nodeType":"text"}],"nodeType":"heading-6"},{"data":{},"content":[{"data":{},"marks":[],"value":"","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"To understand why multi-omics data are so important, think of it this way: Even under the current paradigm, when you’re sick, your doctors don’t just look at a single health parameter, they look at many.(5) What are your symptoms? Have you been exposed to any pathogens? Are you in a high-risk category? How are your vitals? What lab test should be ordered? Etcetera, etcetera. Multi-omics research is about doing the same thing at the cellular level to gain a deeper understanding of disease mechanisms and drug response. So instead of just looking at the static genome alone, we’re looking at multiple dynamic factors across various cells and events in order to scrutinize potentially relevant cellular processes, such as regulatory networks or cell signaling pathways of interest.(6)","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"As the National Human Genome Research Institute (NHGRI) explains, “While single ‘omic analyses have produced valuable insights, recent studies have shown that integrative (or multi-omic) analysis approaches can improve the classification of disease into clinically relevant subgroups and potentially identify biomarkers of health or disease. Multi-omic analyses can also help define relationships among omic data types to unravel biological networks regulating transitions from health to disease.(7) The NHGRI is leading a multi-omics initiative that will “produce consensus approaches, best practices, and standards that can be generalized across diseases and populations. It will also generate a standardized and harmonized dataset for general research use available through controlled-access processes as well as a portal for visualization. Ultimately, this program will enhance the utility of ‘omic technologies in understanding the biology of health and disease.”(7)","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Certainly, a major hurdle to multi-omic research is the multitude of research groups, data types and technologies involved in the various specialty areas. As one review in Frontiers in Genetics points out, “The mushrooming of a myriad of tools, datasets, and approaches tends to inundate the literature and overwhelm researchers new to the field.\"(8)","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Proteomics: One multi-omic example ","nodeType":"text"}],"nodeType":"heading-2"},{"data":{},"content":[{"data":{},"marks":[],"value":"By digging a bit farther into just one of these “omics”—let’s say proteomics—we can show how the data problem just keeps snowballing. Figure 3 was created by a group of proteomics researchers to provide a high-level summary of the assay and mass spec technologies, quantitative analysis workflows, and data integration requirements that go into making proteomics data actionable in a clinical setting. Each of the individual elements is incredibly complicated on its own. As noted by the authors, marrying all these things together will demand substantial work, but that work is worthwhile because proteomics holds incredible promise to complement genetics and “provide insight about the dynamic behavior of proteins as they represent intermediate phenotypes.”(9)","nodeType":"text"}],"nodeType":"paragraph"},{"data":{"target":{"metadata":{"tags":[],"concepts":[]},"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"pzdvlnh8omga"}},"id":"2HG0f7weN1YB3OBUCdVk0G","type":"Asset","createdAt":"2023-05-03T20:59:26.822Z","updatedAt":"2023-05-03T20:59:26.822Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"publishedVersion":4,"revision":1,"locale":"en-US"},"fields":{"title":"multi omics discovery","description":"","file":{"url":"//images.ctfassets.net/pzdvlnh8omga/2HG0f7weN1YB3OBUCdVk0G/8cbccd1063479c13d8c5a16ed2f48b78/mult_omics_discovery.png","details":{"size":154696,"image":{"width":581,"height":640}},"fileName":"mult omics discovery.png","contentType":"image/png"}}}},"content":[],"nodeType":"embedded-asset-block"},{"data":{},"content":[{"data":{},"marks":[],"value":"Figure 3: General workflow for applying quantitative proteomics in the clinical setting.\n (Rojo, A.C., Heylen, D., Aerts, J. et al. Front Phyisol. [12] 2021)","nodeType":"text"}],"nodeType":"heading-6"},{"data":{},"content":[{"data":{},"marks":[],"value":"","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"While proteomics represents just one example, it is safe to say that multi-omics research overall creates incredible demand for an","nodeType":"text"},{"data":{"uri":"https://www.dotmatics.com/platform"},"content":[{"data":{},"marks":[{"type":"underline"}],"value":" R\u0026D data platform","nodeType":"text"}],"nodeType":"hyperlink"},{"data":{},"marks":[],"value":" that prioritizes data flow and not just workflow. Research teams will need an infrastructure that can integrate the various specialty tools needed for different types of -omics research, while also helping to harmonize the varied data coming from those tools and ensure those data are ","nodeType":"text"},{"data":{"uri":"https://www.dotmatics.com/fair-data-principles-drive-better-scientific-r-and-d"},"content":[{"data":{},"marks":[{"type":"underline"}],"value":"FAIR","nodeType":"text"}],"nodeType":"hyperlink"},{"data":{},"marks":[],"value":" and ready for deeper analysis using predictive modeling tools that will uncover key connections between cellular processes and patient experiences.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Data Technology is Key to Successful Precision Medicine","nodeType":"text"}],"nodeType":"heading-2"},{"data":{},"content":[{"data":{},"marks":[],"value":"Our quick review of multi-omics research, and specifically proteomics, has only scratched the surface on the complexity of the technology, data, and collaboration demands presented by precision medicine. Imagine how these demands will blossom when adding in other areas of “omics” research, and then on top of that adding all the additional types of multimodal biomedical and patient data that need to be considered.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"One thing is clear—data technology will be key to making precision medicine a reality. According to a recent Forbes article contributed by Weill Cornell Medicine, “There are around 10,000 diseases that affect humans – each of whom, it should be noted, currently generate around 80 megabytes each year in imaging and electronic medical record data. Powerful technology is critical for precision medicine, because the numbers are not on the side of personalization.”(10)","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Even in cancer, where precision medicine first gained its footing and has delivered some life-saving breakthroughs for individual patients, the overall impact is mixed.(11-14) A 2018 report from JAMA Oncology indicates that only a small fraction of cancer patients is eligible for precision medications, and of all eligible patients, the overall health impact is often modest.(11-12) A 2021 single-institution retrospective analysis of patients with tumors who underwent genetic variation testing showed more positive results with a disease control rate of around 41%.(13) In an environment where cost is also an influencing factor for patients, policy makers, and insurers, continued improvement of both patient-treatment odds and overall cost-benefit-payoff is imperative. A key way of getting there will be positioning ourselves to best use all the data we have at our disposal.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"While, for now, the cumulative effect of precision medicine may be more of a trickle than a flood, work to improve its impact, not just in cancer but in a wide range of conditions, is multifaceted. Aside from a trend toward multi-omics research and more comprehensive data-rich patient profiles, there is work being done to translate all that data into practice. For example, statisticians at the University of North Carolina at Chapel Hill and North Carolina State University at Raleigh have built statistical models to help formalize an optimal dynamic treatment regime.(15) The models comprise a sequence of decision rules, one per decision point, which map patient information to a recommended action, such prescribing a certain drug at a specific dose or time. The complexity of these statistical models demonstrates the complexity of the data at play.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"While we may have a long road ahead until precision medicine becomes standard care, companies like","nodeType":"text"},{"data":{"uri":"https://www.dotmatics.com/"},"content":[{"data":{},"marks":[],"value":" ","nodeType":"text"},{"data":{},"marks":[{"type":"underline"}],"value":"Dotmatics","nodeType":"text"}],"nodeType":"hyperlink"},{"data":{},"marks":[],"value":" are working to create solutions that help researchers harmonize their data and science and move the dial forward.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[{"type":"bold"}],"value":"See how you can leverage the ability to contextualize your findings with ","nodeType":"text"},{"data":{"uri":"https://www.dotmatics.com/solutions/data-intelligence-lab-software"},"content":[{"data":{},"marks":[{"type":"bold"}],"value":"data intelligence software","nodeType":"text"}],"nodeType":"hyperlink"},{"data":{},"marks":[{"type":"bold"}],"value":", so you can translate your data into practice.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"References","nodeType":"text"}],"nodeType":"heading-2"},{"data":{},"content":[{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Global market for personalized medicine from 2015 to 2022, by product (in billion U.S. dollars). 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(9) 2016. https://doi.org/10.1016/j.molp.2016.04.017","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Zhang, Y.","nodeType":"text"},{"data":{"uri":"https://www.forbes.com/sites/forbestechcouncil/2021/10/22/why-multi-omics-is-the-future-of-biological-analysis/?sh=3e8f62552203"},"content":[{"data":{},"marks":[],"value":" ","nodeType":"text"},{"data":{},"marks":[{"type":"underline"}],"value":"Why Multi-Omics Is The Future Of Biological Analysis","nodeType":"text"}],"nodeType":"hyperlink"},{"data":{},"marks":[],"value":". Forbes. October 22, 2021. 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[Accessed April 13, 2023]","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"","nodeType":"text"},{"data":{"uri":"https://www.genome.gov/research-funding/Funded-Programs-Projects/Multi-Omics-for-Health-and-Disease"},"content":[{"data":{},"marks":[{"type":"underline"}],"value":"Multi-Omics for Health and Disease (Multi-Omics).","nodeType":"text"}],"nodeType":"hyperlink"},{"data":{},"marks":[],"value":" National Human Genome Research Institute. 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","marks":[],"data":{}}]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"Databricks and Dotmatics are addressing this challenge head-on, leveraging innovative technologies to streamline workflows and empower users to focus on meaningful, value-driven work. We think that scientists should be able to jump straight into their most impactful data science projects. That’s why Databricks has integrated foundational capabilities into its platform, such as built-in quality controls and the innovative Lakehouse Monitoring system—an AI-driven tool for anomaly detection—which allow for seamless orchestration across pipelines. 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","marks":[{"type":"bold"}],"data":{}},{"nodeType":"text","value":"That means that behind the scenes, large language models (LLMs) auto-describe tables and columns, simplifying workflows for users so they can focus on the more exciting aspects of discovery. ","marks":[],"data":{}}]}]},{"nodeType":"list-item","data":{},"content":[{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"LLMs can combine vast amounts of data.","marks":[{"type":"bold"}],"data":{}},{"nodeType":"text","value":" Imagine using AI to harmonize medical literature with internal research data, plus all of the instrument data Dotmatics is able to bring to bear—that creates a really powerful opportunity to build AI powered assistants. Or consider the possibilities of using AI with pre-training models, such as geneformer models to better understand gene expression and network biology. 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You want to create a framework that allows for opinions to be heard and trade-offs to be evaluated. Before diving into tools, it’s critical to first understand: what are the business objectives and outcomes that the collaboration is trying to achieve? Once everyone is clear on the goals, technology can facilitate the necessary debates and decision-making around the data and trade-offs.","marks":[],"data":{}}]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"Control or security governance","marks":[{"type":"bold"}],"data":{}},{"nodeType":"text","value":" is a common challenge that organizations lack when sharing data. This often means that collaborators must learn custom APIs or use tools they’re not comfortable with, hindering effective data analysis. For collaboration to thrive, teams need to be able to use their preferred tools—whether it's a BI tool or Python. A governance system like Unity Catalog lets organizations securely share data while maintaining flexibility, giving teams confidence to work with data in their chosen tools, which is key to kickstarting successful collaboration.","marks":[],"data":{}}]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"Low-code technology","marks":[{"type":"bold"}],"data":{}},{"nodeType":"text","value":" is a key driver of successful collaboration. It’s not just about simplifying software development; it’s about democratizing the process and bringing key stakeholders from different functions to the same table. The goal is to allow business, science and IT stakeholders to actively participate in building software, so that they can quickly see whether it meets their needs before committing to months of development. This approach accelerates agility and ensures the project is on track to meet user requirements.","marks":[],"data":{}}]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"Data management ","marks":[{"type":"bold"}],"data":{}},{"nodeType":"text","value":"plays a crucial role. A low-code platform, particularly one built on technologies like Databricks, centralizes data and offers visibility across various sources. With AI-powered, configurable interfaces, everyone can interpret data through a consistent lens, preventing different functions from interpreting the same data set differently. This alignment helps foster better collaboration.","marks":[],"data":{}}]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"Change management ","marks":[{"type":"bold"}],"data":{}},{"nodeType":"text","value":"is also key. Researchers and teams are used to their workflows, and shifting those habits can be challenging. It’s important to slow down, focus on collaboration, and recognize that a bit of delay upfront can lead to faster progress in the long run. As research boundaries evolve, particularly in fields like drug discovery, enabling seamless collaboration is more important than ever.","marks":[],"data":{}}]},{"nodeType":"heading-2","data":{},"content":[{"nodeType":"text","value":"On-demand webinar: Building breakthroughs - harnessing data and AI for innovation","marks":[],"data":{}}]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"If you’re interested in learning more, check out the full webinar, ","marks":[],"data":{}},{"nodeType":"hyperlink","data":{"uri":"https://www.dotmatics.com/webinars/building-breakthroughs-harnessing-data-and-ai-for-innovation"},"content":[{"nodeType":"text","value":"Building Breakthroughs - Harnessing Data and AI for Innovation","marks":[{"type":"underline"}],"data":{}}]},{"nodeType":"text","value":". Hear our panel of experts dive deeper into the ways in which the Dotmatics and Databricks partnership is helping R\u0026D teams optimize their:","marks":[],"data":{}}]},{"nodeType":"unordered-list","data":{},"content":[{"nodeType":"list-item","data":{},"content":[{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"Data acquisition ","marks":[{"type":"bold"}],"data":{}},{"nodeType":"text","value":"to consistently and securely collect data and metadata at scale","marks":[],"data":{}}]}]},{"nodeType":"list-item","data":{},"content":[{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"Data infrastructure","marks":[{"type":"bold"}],"data":{}},{"nodeType":"text","value":" to better organize and manage multimodal data and metadata ","marks":[],"data":{}}]}]},{"nodeType":"list-item","data":{},"content":[{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"Data readiness ","marks":[{"type":"bold"}],"data":{}},{"nodeType":"text","value":"to reduce the burden of precursory AI data preparation processes","marks":[],"data":{}}]}]},{"nodeType":"list-item","data":{},"content":[{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"Data integration ","marks":[{"type":"bold"}],"data":{}},{"nodeType":"text","value":"to enable advanced analysis of complex heterogeneous data with speciality scientific tools and multimodal AI/ML algorithms","marks":[],"data":{}}]}]},{"nodeType":"list-item","data":{},"content":[{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"Data governance","marks":[{"type":"bold"}],"data":{}},{"nodeType":"text","value":" to safeguard and track data use across teams and within AI models","marks":[],"data":{}}]}]}]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"Panelists include:","marks":[],"data":{}}]},{"nodeType":"unordered-list","data":{},"content":[{"nodeType":"list-item","data":{},"content":[{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"Kalim Saliba, Dotmatics, Chief Product Officer","marks":[],"data":{}}]}]},{"nodeType":"list-item","data":{},"content":[{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"Michael Sanky, Databricks, VP, Healthcare \u0026 Life Sciences GTM","marks":[],"data":{}}]}]},{"nodeType":"list-item","data":{},"content":[{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"Scott Stunkel, Dotmatics, VP, Engineering - Luma","marks":[],"data":{}}]}]},{"nodeType":"list-item","data":{},"content":[{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"David Stodder, The Data Warehousing Institute (TDWI), Research Fellow","marks":[],"data":{}}]}]}]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"","marks":[],"data":{}}]}]},"thumbnail":{"metadata":{"tags":[],"concepts":[]},"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"pzdvlnh8omga"}},"id":"1msrUSOmUaPEW2kaZ4aVtz","type":"Asset","createdAt":"2024-12-18T17:52:15.681Z","updatedAt":"2024-12-18T17:52:15.681Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"publishedVersion":7,"revision":1,"locale":"en-US"},"fields":{"title":"Webinar-Building-Breakthroughs-Thumbnail","description":"","file":{"url":"//images.ctfassets.net/pzdvlnh8omga/1msrUSOmUaPEW2kaZ4aVtz/b961e17fe0abb50e0ef490e6e51c7dad/Weinar-Breakthroughs-VideoThumbnail.png","details":{"size":359398,"image":{"width":1280,"height":720}},"fileName":"Weinar-Breakthroughs-VideoThumbnail.png","contentType":"image/png"}}},"timeToRead":"10 min","theme":"Grey","metaTitle":"Harnessing data \u0026 AI for scientific R\u0026D | Dotmatics \u0026 Databricks","metaDescription":"Discover how Dotmatics and Databricks are transforming scientific R\u0026D by unifying data, AI, and automation. Learn how to overcome data silos, improve data trust, and accelerate discovery with AI-driven insights.","searchTags":["Luma","Databricks"],"space":{"sys":{"type":"Link","linkType":"Space","id":"pzdvlnh8omga"}},"id":"TFUKGjljMbqEDUE9jahBI","type":"Entry","createdAt":"2025-02-04T18:35:19.427Z","updatedAt":"2025-02-04T20:48:10.184Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"publishedVersion":60,"revision":7,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"postPage"}},"locale":"en-US"},{"title":"3 Customer Trends We’re Watching in 2025","slug":"3-customer-trends-in-2025","date":"2024-12-18T04:00+02:00","showPostNavigation":false,"content":{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"In 2024, our role as a trusted partner to our customers has been front and center. As we shift into the new year, ","nodeType":"text"},{"data":{"uri":"https://www.dotmatics.com/blog/big-2024-plans-building-the-first-multimodal-r-and-d-platform"},"content":[{"data":{},"marks":[{"type":"underline"}],"value":"our focus remains","nodeType":"text"}],"nodeType":"hyperlink"},{"data":{},"marks":[],"value":" on delivering tangible scientific product innovation and value across all our product brands. With the addition of Dotmatics Luma, we’re not only breaking down data silos, but we’re also enabling seamless interoperability and powering comprehensive, scientifically precise, multimodal workflows. These are critical steps for our customers to fully harness the transformative potential of AI-driven discovery.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"We know that our customers are doing more with less resources these days. This theme has continued over the last few years of pandemic recovery efforts. Efficiency remains key. AI has the promise to play an increasingly important role in freeing up time and resources, yet it’s not the full story. Because although companies are searching for new tools to help them, they’re also getting creative internally—finding ways to reallocate existing resources, often replacing technologies and cutting costs to open up more budgets.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Overall, there are three meta trends we’re hearing most frequently as being mission critical to our customers. ","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[{"type":"bold"}],"value":"Trend #1: To make R\u0026D more efficient, companies need a Lab-in-a-Loop","nodeType":"text"}],"nodeType":"heading-2"},{"data":{},"content":[{"data":{},"marks":[],"value":"We know that for AI to create meaningful impact in life sciences, the drug discovery process must evolve to become a Lab-in-a-Loop, where R\u0026D data and clinical data across applications, databases, and lab equipment are ingested, centralized, and then used to create models of prediction for the next set of experiments. I think most scientists agree. ","nodeType":"text"},{"data":{"uri":"https://www.dotmatics.com/blog/lab-in-a-loop-bridging-the-bench-and-ai-with-dotmatics-luma"},"content":[{"data":{},"marks":[{"type":"underline"}],"value":"The goal is an iterative cycle","nodeType":"text"}],"nodeType":"hyperlink"},{"data":{},"marks":[],"value":". AI models, trained on R\u0026D and clinical data, can predict and refine experiments, creating a more efficient interplay between the wet lab where experiments are physically performed and a dry lab where AI predicts which experiments will be most successful, or even predicts the form therapeutic molecules should take. The result is a faster and more efficient drug discovery process.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Today, there are many different terms for platforms that strive to fill this need, and it’s early enough that there is no consensus on terminology. Some experts call it a Unified Lab Informatics Platform (ULIP), or a multimodal scientific intelligence platform. Regardless of nomenclature, such a platform is essential for modern labs because it brings together scientific precision in molecular representation and lineage, data management and processing, specialized scientific software, and adaptive workflows into a single, streamlined system. It integrates data from diverse sources, eliminating silos and enabling seamless collaboration among teams. It boosts productivity and ensures faster, more accurate decision-making by automating routine tasks and providing real-time ingestion, contextualization, and access to harmonized data.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Since we ","nodeType":"text"},{"data":{"uri":"https://www.dotmatics.com/news/new-dotmatics-luma-scientific-data-platform"},"content":[{"data":{},"marks":[{"type":"underline"}],"value":"launched Dotmatics Luma","nodeType":"text"}],"nodeType":"hyperlink"},{"data":{},"marks":[],"value":" in 2023, followed by ","nodeType":"text"},{"data":{"uri":"https://www.dotmatics.com/news/dotmatics-luma-lab-connect"},"content":[{"data":{},"marks":[{"type":"underline"}],"value":"Lab Connect","nodeType":"text"}],"nodeType":"hyperlink"},{"data":{},"marks":[],"value":", the Luma data and instrument ingestion engine, earlier this year, we’ve seen tremendous interest and activity using the platform. We already have more than 2,100 connected instruments that have parsed over 47 billion data records and 300 terabytes of data. That’s massive. And as more and more labs embrace the potential of AI-driven discovery, Luma will serve as a future-ready foundation, supporting their innovation and scalability.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"One ","nodeType":"text"},{"data":{"uri":"https://www.dotmatics.com/case-studies/beyond-json-centralizing-modeling-and-aggregating-instrument-data-to-enable"},"content":[{"data":{},"marks":[{"type":"underline"}],"value":"major multinational pharmaceutical company","nodeType":"text"}],"nodeType":"hyperlink"},{"data":{},"marks":[],"value":" set a goal to deploy Luma to 1000 instruments within a year, and within the first six months had already connected 2000+ instruments—4X faster than expected. In fact, within 10 minutes of seeing Luma at work, team leaders said they realized it was categorically different than any other solution on the market. That’s because of its seamless linkage of the output processing with the extraordinarily flexible, yet well-governed data model, its advanced dataflow and data analysis capabilities, and its quick deployability.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Similarly, the oncology R\u0026D group ","nodeType":"text"},{"data":{"uri":"https://www.dotmatics.com/case-studies/modernizing-flow-cytometry-analysis-to-accelerate-oncology-r-and-d"},"content":[{"data":{},"marks":[{"type":"underline"}],"value":"at a top US pharmaceutical company","nodeType":"text"}],"nodeType":"hyperlink"},{"data":{},"marks":[],"value":" recently deployed our Luma Flow Cytometry Workflow. It took Dotmatics less than 10 hours to remotely deploy an environment to parse the team’s data using Luma Lab Connect. Rollout was also fast, with five instruments and 20 users connected and onboarded in one week. Each scientist is now saving multiple hours every week thanks to better connection of instrument files to flow cytometry software, plus the countless development hours saved by putting the brakes on a costly home-grown solution.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[{"type":"bold"}],"value":"Trend #2: Multimodal discovery is becoming a reality","nodeType":"text"}],"nodeType":"heading-2"},{"data":{},"content":[{"data":{},"marks":[],"value":"Companies are increasingly adopting the approach of addressing medical needs using whichever mode of action proves effective. This means they need ","nodeType":"text"},{"data":{"uri":"https://www.dotmatics.com/whitepapers/rise-of-multimodal-r-and-d-and-ai"},"content":[{"data":{},"marks":[{"type":"underline"}],"value":"a truly multimodal informatics system","nodeType":"text"}],"nodeType":"hyperlink"},{"data":{},"marks":[],"value":" now more than ever. They need tools that can handle conjugate therapies, that can tackle target-focused research regardless of the applied therapeutic modality, and that provide a more robust and economical solution across the therapeutic modalities due to not needing to stitch together multiple disparate solutions.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[{"type":"bold"}],"value":"Because multimodal research is inherently more varied, the platforms that can support it must be more flexible and extensible","nodeType":"text"},{"data":{},"marks":[],"value":". Developing a platform with flexibility and extensibility in mind lets us and our customers adapt our software to new, as of yet undiscovered modes of research and molecule types of tomorrow. The platform reflects and enables how scientific discovery actually takes place.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"}],"nodeType":"unordered-list"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Because of this, our ","nodeType":"text"},{"data":{"uri":"https://www.dotmatics.com/news/luma-antibody-and-protein-engineering-solution"},"content":[{"data":{},"marks":[{"type":"underline"}],"value":"newly introduced Geneious Luma","nodeType":"text"}],"nodeType":"hyperlink"},{"data":{},"marks":[],"value":" can support any sequence-based modality—monoclonal antibodies, multispecific antibodies, antibody drug conjugates, CAR-T cell discovery, siRNA, CRISPR therapeutics, and vaccine discovery. Geneious Luma enables researchers to use the advanced bioinformatics, molecular biology, and antibody discovery capabilities of ","nodeType":"text"},{"data":{"uri":"https://www.dotmatics.com/solutions/geneious-prime"},"content":[{"data":{},"marks":[{"type":"underline"}],"value":"Geneious Prime","nodeType":"text"}],"nodeType":"hyperlink"},{"data":{},"marks":[],"value":" and ","nodeType":"text"},{"data":{"uri":"https://www.dotmatics.com/solutions/geneious-biologics"},"content":[{"data":{},"marks":[{"type":"underline"}],"value":"Geneious Biologics","nodeType":"text"}],"nodeType":"hyperlink"},{"data":{},"marks":[],"value":" to design, qualify, annotate, and filter sequence, and assay data. Seamlessly working together with Luma, researchers can execute their cloning, expression, and purification tasks using Luma’s Adaptive Workflow capabilities. ","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"blockquote"},{"data":{},"content":[{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[{"type":"bold"}],"value":"Multimodal biologics research is divided into two bins: conjugates and target-based discovery.","nodeType":"text"},{"data":{},"marks":[],"value":" Multimodal research has expanded the horizons of protein engineering. This approach integrates both a confluence of datasets and methodologies, and requires a platform that is capable of supporting a variety of therapeutic modalities. In the context of antibody engineering, multimodal research includes both conjugate development and target-based discovery. ","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"}],"nodeType":"unordered-list"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Conjugates involve combining different research modalities to create novel molecules, while target-based discovery focuses on identifying the best modality for a specific target within a unified platform. Conjugates enable mixing different modalities of research to arrive at a combination molecule. Target-based discovery, which focuses on identifying the most suitable modality for a specific target, requires an integrated platform to streamline decision-making. Without a unified system, researchers must bounce around different data sets and software tools. Both conjugate development and target-based discovery are heavily reliant on DNA/RNA/protein sequence analyses throughout the R\u0026D process and final protein production, which is where Geneious Luma is a leader in the biotech space. ","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"blockquote"},{"data":{},"content":[{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[{"type":"bold"}],"value":"Multimodal research platforms reduce the need to cobble together disparate systems","nodeType":"text"},{"data":{},"marks":[],"value":". When paired with a low-code environment that creates greater business agility because business and IT can collaborate iteratively on outcomes. With Luma, both scientific R\u0026D and business data science can take place using one platform. And because scientific data requires context and lineage information to rationalize, capturing it in situ within a unified platform, means spending less time on expensive downstream tasks such as data collating and cleansing…where it’s often too late to identify the appropriate context. \n","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[{"type":"bold"}],"value":"Importantly, multimodal research might open up the ability to train models on outcomes that span modalities;","nodeType":"text"},{"data":{},"marks":[],"value":" it’s foreseeable that you could predict the efficacy of a particular type of molecule against a target based on historical data across all targets and modalities under various conditions. This would allow researchers to gain deeper insights and make cross-disciplinary connections that were previously impossible.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"}],"nodeType":"unordered-list"},{"data":{},"content":[{"data":{},"marks":[],"value":"What’s exciting with Dotmatics Luma is that in addition to being a platform for multimodal discovery, it addresses the needs of ","nodeType":"text"},{"data":{"uri":"https://www.dotmatics.com/solutions/biologics-discovery"},"content":[{"data":{},"marks":[{"type":"underline"}],"value":"biologics-based research","nodeType":"text"}],"nodeType":"hyperlink"},{"data":{},"marks":[],"value":"—the fastest growing research area—far better than anything available to the industry today. ","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[{"type":"bold"}],"value":"Trend # 3: AI at the tipping point: driving tangible outcomes","nodeType":"text"}],"nodeType":"heading-2"},{"data":{},"content":[{"data":{},"marks":[],"value":"Two years ago the AI hype cycle created such a frenzy, but there wasn’t a lot of clarity on ","nodeType":"text"},{"data":{},"marks":[{"type":"italic"}],"value":"how ","nodeType":"text"},{"data":{},"marks":[],"value":"and ","nodeType":"text"},{"data":{},"marks":[{"type":"italic"}],"value":"when ","nodeType":"text"},{"data":{},"marks":[],"value":"the technology might impact R\u0026D broadly. Today, the intersection of AI and life sciences is no longer a futuristic concept; it's happening now. Our customers are at various stages along the path of transformation, and are looking for the right tools to help. There is a strong incentive to introduce new resources that put the power in scientists' hands to decrease the burden on data science and IT, and significantly expand the value of AI/ML. ","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Likewise, multispecific antibodies provide a big opportunity to develop innovative treatments that improve patient care for complex diseases like cancer and autoimmune disorders. For that to happen efficiently, researchers will need tools enabling the design and generation of multispecific antibody formats and the execution of predictive ML models to score potential multispecific antibodies, so that they can proceed with only those that are most promising. This is why we recently introduced the ","nodeType":"text"},{"data":{"uri":"https://www.dotmatics.com/luma/antibody-and-protein-engineering"},"content":[{"data":{},"marks":[{"type":"underline"}],"value":"Luma Antibody \u0026 Protein Engineering solution","nodeType":"text"}],"nodeType":"hyperlink"},{"data":{},"marks":[],"value":", a comprehensive end-to-end solution for streamlining the antibody R\u0026D process, with an emphasis on monoclonal and multispecific antibodies.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"The pinnacle of the AI journey is \"Composite AI,\" where scientists leverage all their data across disciplines, including cascading layers and multiple types of AI, to drive simulations, predictions, and novel recommendations. For example, scientists can leverage auto-gated flow cytometry results produced in OMIQ that are then used alongside other assay data to train down-stream models for molecular liability calculations. It’s this coming together of multiple types of AI-processed or predicted data where the true potential of AI for drug discovery is realized, and where Dotmatics Luma plays its most essential role.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"This is also where big data is critical. The rule for AI/ML is that you need a LOT of data before the modeling can be useful. The increasing availability of data from many sources is driving biotech research, helping to find and interrogate old and new targets, to refine treatment approaches, all toward improving patient outcomes, especially as patient data are related to experimental data. That means seeking out ","nodeType":"text"},{"data":{"uri":"https://www.dotmatics.com/whitepapers/ai-driven-pharma-r-and-d"},"content":[{"data":{},"marks":[{"type":"underline"}],"value":"smart integrated data strategies","nodeType":"text"}],"nodeType":"hyperlink"},{"data":{},"marks":[],"value":" that enhance an organization's existing software spend. In the coming year, customers must focus on improving their data strategies to boost their AI adoption and effectiveness.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Finally on the regulatory front, the introduction of AI model governance will become a major customer priority in 2025, and beyond. We need to ensure models are accurate, ethical, and compliant with regulatory standards, to reduce risks of bias and errors in drug development. This is crucial for maintaining data integrity, patient safety, and public trust in AI-driven advancements in drug discovery. There is also increased importance on traceability of data, especially in the design of new proteins. This includes the need for lineage tracking back to ","nodeType":"text"},{"data":{},"marks":[{"type":"italic"}],"value":"in silico","nodeType":"text"},{"data":{},"marks":[],"value":" designs.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"We’ve got a ton of work to do. But as we head into 2025, we’re excited to partner with our customers to tackle these trends head-on—breaking down silos, enabling smarter workflows, and driving discovery faster than ever. ","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"The future of science is bright, and we’re just getting started.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"document"},"thumbnail":{"metadata":{"tags":[],"concepts":[]},"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"pzdvlnh8omga"}},"id":"36qtaN8koyUfpYd3kfwDgj","type":"Asset","createdAt":"2024-12-18T22:40:15.430Z","updatedAt":"2024-12-18T22:40:15.430Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"publishedVersion":5,"revision":1,"locale":"en-US"},"fields":{"title":"2025-trends","description":"","file":{"url":"//images.ctfassets.net/pzdvlnh8omga/36qtaN8koyUfpYd3kfwDgj/4cec74dd9790234da8fa35c229a090dd/iStock-1845293358.jpg","details":{"size":716603,"image":{"width":2390,"height":1255}},"fileName":"iStock-1845293358.jpg","contentType":"image/jpeg"}}},"author":{"metadata":{"tags":[],"concepts":[]},"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"pzdvlnh8omga"}},"id":"2DjFi295CrcwsLfidTCmuA","type":"Entry","createdAt":"2022-12-22T16:32:52.855Z","updatedAt":"2025-03-05T14:12:58.633Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"publishedVersion":10,"revision":4,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"authtor"}},"locale":"en-US"},"fields":{"name":"Kalim Saliba, Chief Product Officer","image":{"metadata":{"tags":[],"concepts":[]},"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"pzdvlnh8omga"}},"id":"5iE5OJ49A5qTbySmjrDzjX","type":"Asset","createdAt":"2022-12-22T16:32:39.192Z","updatedAt":"2022-12-22T16:32:39.192Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"publishedVersion":4,"revision":1,"locale":"en-US"},"fields":{"title":"Kalim Saliba Headshot","description":"","file":{"url":"//images.ctfassets.net/pzdvlnh8omga/5iE5OJ49A5qTbySmjrDzjX/f4ce3cae45c87bfd34e19b8788f6acb5/kalim_saliba.jpeg","details":{"size":64773,"image":{"width":750,"height":750}},"fileName":"kalim saliba.jpeg","contentType":"image/jpeg"}}}}},"timeToRead":"10 min","theme":"Grey","metaTitle":"3 Customer Trends We’re Watching in 2025","metaDescription":"Discover the top three trends shaping life sciences R\u0026D in 2024: Lab-in-a-Loop efficiency, multimodal discovery platforms, and the transformative power of AI. ","searchTags":["AI","Lab-in-a-loop","Luma","Multimodal R\u0026D"],"space":{"sys":{"type":"Link","linkType":"Space","id":"pzdvlnh8omga"}},"id":"1US6fKkrinIHbenL0V1E2S","type":"Entry","createdAt":"2024-12-18T22:52:03.018Z","updatedAt":"2025-02-24T10:42:42.701Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"publishedVersion":49,"revision":5,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"postPage"}},"locale":"en-US"},{"title":"12 Must-Read Dotmatics Blogs for Driving Scientific Innovation","slug":"2024-must-read-blogs","date":"2024-12-17T04:00+02:00","showPostNavigation":false,"content":{"nodeType":"document","data":{},"content":[{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"As 2024 comes to a close, we're reflecting on a transformative year in scientific R\u0026D. At Dotmatics, we’ve explored cutting-edge trends, shared expert insights, and showcased how technology is driving progress across biologics, small molecules, AI, and digital transformation. In this roundup, we present the ","marks":[],"data":{}},{"nodeType":"text","value":"Best Blogs of 2024","marks":[{"type":"italic"}],"data":{}},{"nodeType":"text","value":" – your go-to list for the year’s most impactful insights from the Dotmatics team.","marks":[],"data":{}}]},{"nodeType":"heading-3","data":{},"content":[{"nodeType":"text","value":"1. Blending Two Worlds: Small Molecule Drugs vs. Biologics","marks":[{"type":"bold"}],"data":{}}]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"The pharmaceutical landscape has evolved, and understanding the synergy between small molecule drugs and biologics is critical for modern R\u0026D. 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This post highlights practical strategies and tools that labs can use to reduce human error and enhance reproducibility.","marks":[],"data":{}}]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"","marks":[],"data":{}},{"nodeType":"hyperlink","data":{"uri":"https://www.dotmatics.com/blog/avoiding-human-errors-in-experiments-for-biologics"},"content":[{"nodeType":"text","value":"Discover strategies to reduce human errors in biologics experiments","marks":[{"type":"underline"}],"data":{}}]},{"nodeType":"text","value":".\n","marks":[],"data":{}}]},{"nodeType":"heading-3","data":{},"content":[{"nodeType":"text","value":"3. Bispecific Antibodies: A Dual Attack on Complex Disease","marks":[{"type":"bold"}],"data":{}}]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"Bispecific antibodies are revolutionizing the treatment of complex diseases. 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This blog outlines the roadmap for R\u0026D teams to embrace digital tools, streamline workflows, and accelerate discoveries.","marks":[],"data":{}}]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"","marks":[],"data":{}},{"nodeType":"hyperlink","data":{"uri":"https://www.dotmatics.com/blog/path-to-digital-transformation"},"content":[{"nodeType":"text","value":"Uncover the roadmap to digital transformation in R\u0026D","marks":[{"type":"underline"}],"data":{}}]},{"nodeType":"text","value":".\n","marks":[],"data":{}}]},{"nodeType":"heading-3","data":{},"content":[{"nodeType":"text","value":"6. Integrated Multi-Omic Data: Powering Precision Medicine","marks":[{"type":"bold"}],"data":{}}]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"Multi-omics data is unlocking personalized treatments and revolutionizing precision medicine. 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This blog shows how better tools and workflows can transform data into actionable insights faster and more efficiently.","marks":[],"data":{}}]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"","marks":[],"data":{}},{"nodeType":"hyperlink","data":{"uri":"https://www.dotmatics.com/blog/flow-cytometry-data-actionable-insights"},"content":[{"nodeType":"text","value":"Transform flow cytometry data into actionable insights","marks":[{"type":"underline"}],"data":{}}]},{"nodeType":"text","value":".\n","marks":[],"data":{}}]},{"nodeType":"heading-3","data":{},"content":[{"nodeType":"text","value":"8. Optimizing Multimodal R\u0026D by Improving Data Flow","marks":[{"type":"bold"}],"data":{}}]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"Managing multimodal data is a common challenge for modern labs. This post provides solutions for seamless data integration, supporting faster research and collaboration.","marks":[],"data":{}}]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"","marks":[],"data":{}},{"nodeType":"hyperlink","data":{"uri":"https://www.dotmatics.com/blog/improving-data-flow-for-multimodal-research"},"content":[{"nodeType":"text","value":"Learn how to improve data flow for multimodal research","marks":[{"type":"underline"}],"data":{}}]},{"nodeType":"text","value":".\n","marks":[],"data":{}}]},{"nodeType":"heading-3","data":{},"content":[{"nodeType":"text","value":"9. How to Plan Your AI Budget Now to Succeed in 2025","marks":[{"type":"bold"}],"data":{}}]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"AI adoption continues to reshape R\u0026D. This blog offers actionable strategies for budgeting AI investments that will deliver measurable ROI and prepare your organization for 2025.","marks":[],"data":{}}]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"","marks":[],"data":{}},{"nodeType":"hyperlink","data":{"uri":"https://www.dotmatics.com/blog/how-to-plan-your-ai-budget-now-to-succeed-in-2025"},"content":[{"nodeType":"text","value":"Plan your AI budget for measurable success in 2025","marks":[{"type":"underline"}],"data":{}}]},{"nodeType":"text","value":".\n","marks":[],"data":{}}]},{"nodeType":"heading-3","data":{},"content":[{"nodeType":"text","value":"10. Three Must-Dos for Tracking Multiformat Antibody R\u0026D","marks":[{"type":"bold"}],"data":{}}]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"Antibody R\u0026D involves managing vast amounts of diverse data formats. 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Dotmatics Reimagines Flow Cytometry","marks":[{"type":"bold"}],"data":{}}]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"See how Dotmatics is redefining flow cytometry workflows with smarter integrations and tools, eliminating manual bottlenecks and accelerating results.","marks":[],"data":{}}]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"","marks":[],"data":{}},{"nodeType":"hyperlink","data":{"uri":"https://www.dotmatics.com/blog/dotmatics-reimagines-flow-cytometry"},"content":[{"nodeType":"text","value":"See how Dotmatics is reimagining flow cytometry workflows","marks":[{"type":"underline"}],"data":{}}]},{"nodeType":"text","value":".\n","marks":[],"data":{}}]},{"nodeType":"heading-3","data":{},"content":[{"nodeType":"text","value":"12. Lab-in-a-Loop: Bridging the Bench and AI with Dotmatics Luma","marks":[{"type":"bold"}],"data":{}}]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"This blog highlights how Dotmatics Luma is connecting laboratory experimentation with AI-driven insights, creating a seamless “lab-in-a-loop” for discovery teams.","marks":[],"data":{}}]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"","marks":[],"data":{}},{"nodeType":"hyperlink","data":{"uri":"https://www.dotmatics.com/blog/lab-in-a-loop-bridging-the-bench-and-ai-with-dotmatics-luma"},"content":[{"nodeType":"text","value":"Learn how Dotmatics Luma bridges the bench and AI","marks":[{"type":"underline"}],"data":{}}]},{"nodeType":"text","value":".\n","marks":[],"data":{}}]},{"nodeType":"heading-2","data":{},"content":[{"nodeType":"text","value":"Looking Ahead to 2025","marks":[{"type":"bold"}],"data":{}}]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"From AI-driven workflows to digital transformation and precision medicine, 2024 has been a year of innovation and progress. 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","marks":[],"data":{}}]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"Visit the ","marks":[],"data":{}},{"nodeType":"hyperlink","data":{"uri":"https://www.dotmatics.com/resources"},"content":[{"nodeType":"text","value":"Dotmatics resource library","marks":[],"data":{}}]},{"nodeType":"text","value":" for the latest breakthroughs and discoveries!","marks":[],"data":{}}]}]},"thumbnail":{"metadata":{"tags":[],"concepts":[]},"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"pzdvlnh8omga"}},"id":"4pRSHN9pUru42YJtciqUM0","type":"Asset","createdAt":"2024-12-17T17:54:59.338Z","updatedAt":"2024-12-17T17:54:59.338Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"publishedVersion":5,"revision":1,"locale":"en-US"},"fields":{"title":"2024 best of blogs","description":"","file":{"url":"//images.ctfassets.net/pzdvlnh8omga/4pRSHN9pUru42YJtciqUM0/6411a7e4936a6ba586d13c0e3742d8db/iStock-1257664166.jpg","details":{"size":1501059,"image":{"width":2121,"height":1414}},"fileName":"iStock-1257664166.jpg","contentType":"image/jpeg"}}},"timeToRead":"6 min","theme":"Grey","metaTitle":"12 Must-Read Dotmatics Blogs for Driving Scientific Innovation","metaDescription":"Explore Dotmatics' best blogs of 2024, covering scientific innovations, digital transformation, AI, biologics, and precision medicine to stay ahead of the scientific R\u0026D trends. 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We know lots of companies want to leverage the power of AI, but they are lacking tools and expertise to bring them to market as finished products within their respective industries. ","marks":[],"data":{}}]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"Companies need control over their data at its source and the AI models that are running on them to ensure security, privacy compliance and accurate decision making. Effectively managing data can offer an organization a competitive edge, reduce costs and provide greater flexibility when integrating applications. ","marks":[],"data":{}}]},{"nodeType":"heading-2","data":{},"content":[{"nodeType":"text","value":"Advancing Scientific Intelligence in Life Sciences","marks":[{"type":"bold"}],"data":{}}]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"That's why we have just announced a ","marks":[],"data":{}},{"nodeType":"hyperlink","data":{"uri":"https://www.dotmatics.com/news/dotmatics-partners-with-databricks"},"content":[{"nodeType":"text","value":"strategic partnership with Databricks","marks":[{"type":"underline"}],"data":{}}]},{"nodeType":"text","value":". Dotmatics is one of the very first \"built-on Databricks\" partners –and our ","marks":[],"data":{}},{"nodeType":"hyperlink","data":{"uri":"https://www.dotmatics.com/luma"},"content":[{"nodeType":"text","value":"Dotmatics Luma","marks":[{"type":"underline"}],"data":{}}]},{"nodeType":"text","value":" is built on a platform of Databricks, leveraging a modern market-leading AI cloud that is optimally designed for scientific data. ","marks":[],"data":{}}]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"Michael Sanky, who is Databricks VP of Healthcare \u0026 Life Sciences said,“Dotmatics Luma exemplifies the transformative potential of building on Databricks, and it’s incredible to watch its adoption among the biopharmaceutical community who are excited to harness the full power of their data.” ","marks":[],"data":{}}]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"Databricks recognizes the value that other companies can bring by building tools on top of its ecosystem, and so it’s committed to bringing the best technology solutions to market within every industry; this includes working with partners like Dotmatics who are building the next generation of data-driven applications for life sciences","marks":[],"data":{}}]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"What does that mean for our customers? 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","marks":[],"data":{}}]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"Today Dotmatics does that through:","marks":[],"data":{}}]},{"nodeType":"unordered-list","data":{},"content":[{"nodeType":"list-item","data":{},"content":[{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"","marks":[],"data":{}},{"nodeType":"hyperlink","data":{"uri":"https://www.dotmatics.com/luma-data-management-platform"},"content":[{"nodeType":"text","value":"Luma Platform","marks":[{"type":"underline"}],"data":{}}]},{"nodeType":"text","value":" helps organize the data and provide governed, configured access and data flow between different customer ecosystems.","marks":[],"data":{}}]}]},{"nodeType":"list-item","data":{},"content":[{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"","marks":[],"data":{}},{"nodeType":"hyperlink","data":{"uri":"https://www.dotmatics.com/luma/lab-instrument-integration-software"},"content":[{"nodeType":"text","value":"Lab Connect","marks":[{"type":"underline"}],"data":{}}]},{"nodeType":"text","value":" serves as a massive data funnel for scientific data into the Luma and Databricks ecosystem, allowing for structured access to hundreds of different types of data from scientific instruments and research tools, including many of our own best-in-class tools.","marks":[],"data":{}}]}]}]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"The result is well organized, secure data ready for Delta Sharing into the customer’s own ecosystem, and ready for AI, BI, or Notebook use cases. 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Once you've built a model you like, MLflow helps save it in a standard way so it can be reused or shared with others. The technology makes it easier to put your trained model into action, whether that's in a web app, a batch processing job, or another system. It provides a centralized place for teams to document and share their work, making it easier for others to understand and build upon your progress.","marks":[],"data":{}}]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"We think our customers will particularly like the ability to apply security and permissions on who may access those models. Plus, it’s super easy to use, requiring one line of code. Basically, any model that’s stored in Unity Catalog can be served right away. One use case that we've been using internally is Chemical Structure Activity prediction. With this method of storing and serving models, it's very easy to run the predictions for every new structure added to the system.","marks":[],"data":{}}]},{"nodeType":"heading-3","data":{},"content":[{"nodeType":"text","value":"Write SQL Queries to Expedite the R\u0026D Process","marks":[{"type":"bold"}],"data":{}}]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"Most people know that AI companies have APIs that you can use to leverage their foundation of large language models (LLMs), but Databricks takes it a step further by integrating those models directly into SQL queries. And that can open up new, unexplored possibilities in the R\u0026D pathway to drug discovery. ","marks":[],"data":{}}]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"Imagine that you have 10,000 description fields and you want to know which ones to look into more closely. 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","marks":[],"data":{}}]},{"nodeType":"heading-3","data":{},"content":[{"nodeType":"text","value":"Enhancing AI with Scientific Context ","marks":[{"type":"bold"}],"data":{}}]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"Similarly, when you are asking a LLM like ChatGPT a question, you can define functions to help it answer more accurately with deeper context. Dotmatics is designing this equivalent for life sciences R\u0026D. We’re developing scientific functions that will allow the AI to execute a number of statistical or scientific functions in the process of answering a question. That includes the ability to give governed, controlled access to your data should you choose to do so. We’re also building the ability to give the AI scientific powers to provide even greater understanding, for example such as statistical calculations, or gating in flow cytometry data. ","marks":[],"data":{}}]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"That could be a real gamechanger. Giving the AI the tools it needs to answer deep, in-field questions about our customers' data is what they need to supercharge their decision-making workflows.","marks":[],"data":{}}]},{"nodeType":"heading-3","data":{},"content":[{"nodeType":"text","value":"Build Confidence with Operational Monitoring for AI ","marks":[{"type":"bold"}],"data":{}}]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"Operational monitoring is essential for ensuring AI systems run reliably and effectively in real-world environments. It’s what catches issues early, like a data pipeline breaking or a model starting to drift from accuracy. In regulated industries, it’s the key to staying compliant and audit-ready. It also helps build trust—people rely on AI that’s consistent and reliable. Simply put, it’s how AI stays useful, ethical, and effective.","marks":[],"data":{}}]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"Databricks recently introduced operational monitoring through an interface that lets you see what the AI was “thinking” at different parts of you asking questions. Dotmatics Luma will be able to uncover how that AI was working using the new Databricks functionality. And though not a customer-facing feature at this time, that level of detail is important to know what the AI was trying to do at different points throughout the process. It provides deep observability into whether the AI is getting answers right or wrong, which equates to what you need to actually make a good product. ","marks":[],"data":{}}]},{"nodeType":"paragraph","data":{},"content":[{"nodeType":"text","value":"This is all just the beginning. 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