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false;" target="_blank" rel="nofollow" class="nolightbox essb-s-bg-flipboard essb-s-bgh-flipboard essb-s-c-light essb-s-hover-effect essb-s-bg-network essb-s-bgh-network" ><span class="essb_icon essb_icon_flipboard"></span><span class="essb_network_name">Flipboard</span></a></li></ul></div></div></div> <div class="essb_morepopup_shadow essb_morepopup_shadow_311456969" onclick="essb.toggle_less_popup('311456969'); return false;"></div></div> <div id="meta2"><p>Posted: 13 September 2024 | <a href="https://www.drugtargetreview.com/content_author/dr-raminderpal-singh-hitchhikers-ai-and-20-15-visioneers/" rel="tag">Dr Raminderpal Singh (Hitchhikers AI and 20/15 Visioneers)</a> | <a href="https://www.drugtargetreview.com/article/152914/part-three-15-pragmatic-guidelines-to-handle-data-quality-issues/#respond" class="comments-link" >No comments yet</a></p> </div> <div class="excerpt"><p>In this four-part series, Dr Raminderpal Singh will discuss the challenges surrounding limited data quality, and some pragmatic solutions. In this third article, he discusses pragmatic guidelines to help support better data quality.</p> </div> <div class="articleImage"> <img data-lazyloaded="1" src="data:image/gif;base64,R0lGODdhAQABAPAAAMPDwwAAACwAAAAAAQABAAACAkQBADs=" width="750" height="450" data-src="https://www.drugtargetreview.com/wp-content/uploads/Data.jpg" class="attachment-large-rectangle size-large-rectangle wp-post-image" alt="Data set" decoding="async" fetchpriority="high" data-srcset="https://www.drugtargetreview.com/wp-content/uploads/Data.jpg 750w, https://www.drugtargetreview.com/wp-content/uploads/Data-375x225.jpg 375w" data-sizes="(max-width: 750px) 100vw, 750px" /><noscript><img width="750" height="450" src="https://www.drugtargetreview.com/wp-content/uploads/Data.jpg" class="attachment-large-rectangle size-large-rectangle wp-post-image" alt="Data set" decoding="async" fetchpriority="high" srcset="https://www.drugtargetreview.com/wp-content/uploads/Data.jpg 750w, https://www.drugtargetreview.com/wp-content/uploads/Data-375x225.jpg 375w" sizes="(max-width: 750px) 100vw, 750px" /></noscript></div>   <p>In the first article in this series, published Wednesday 14 August, we discussed the importance that data quality plays in the effectiveness of data analyses by Machine Learning (ML) and AI. In part two, we looked at the impact that poor data quality can have on ML models. Here, we outline 15 pragmatic guidelines to ensure better data quality in early drug discovery and to identify potential issues with compromised data.</p> <ol> <li>Data collection and entry</li> </ol> <p>Standardisation, by implementing standardised operating procedures (SOPs) for data collection and entry, is a key consideration. This includes consistent use of units, naming conventions, and data formats. The team involved in data collection should be trained on the importance of data quality and the specific protocols to follow in order to minimise errors.</p> <ol start="2"> <li>Data validation</li> </ol> <p>Automated checks should be carried out with automated validation scripts, which check for common issues such as missing values, duplicates, outliers and inconsistencies in units or formats. Furthermore, manual reviews of a subset of the data should be performed periodically to identify any issues that automated checks might miss.</p><div class="drugt-b1b1b1fa0a7202336a24e9c9cd001aa6 drugt-content" id="drugt-b1b1b1fa0a7202336a24e9c9cd001aa6"></div> <ol start="3"> <li>Data cleaning</li> </ol> <p>A strategy should be developed for handling missing data, such as deciding when to use imputation, exclude data points, or flag datasets for further investigation. Also, methods should be implemented to identify and investigate outliers, determining whether they represent true variability or errors.</p> <ol start="4"> <li>Data integration</li> </ol> <p>It is essential to ensure that data from different sources or experiments are harmonised before integration. This includes reconciling different naming conventions, units and formats. Consistency and correctness can be checked by cross validation, in which cross-referencing methods validate integrated datasets.</p> <ol start="5"> <li>Data documentation</li> </ol> <p>Researchers should maintain detailed metadata for each dataset, including information about the origin, collection method, and any preprocessing steps. This helps in tracking data provenance and understanding the context. Also, version control systems for datasets should be used to track changes and ensure that any modifications are well documented and reversible.</p> <ol start="6"> <li>Data monitoring</li> </ol> <p>Data should be monitored continuously, recording quality metrics like completeness, accuracy, and consistency, throughout the data lifecycle. Moreover, automated alerts can be set up to notify relevant personnel if data quality metrics fall below predefined thresholds.</p> <ol start="7"> <li>Data auditing</li> </ol> <p>Conduct regular data audits to assess the overall quality of your datasets. This involves checking for adherence to data quality standards and identifying any systemic issues. Also, maintain audit trails that log all data processing steps, transformations, and any changes made to the data to ensure traceability and accountability.</p> <ol start="8"> <li>Bias and variability checks</li> </ol> <p>Regularly assess your datasets for potential biases, such as over-representation of certain chemical scaffolds or biological targets. Statistical techniques should be used to quantify bias and take corrective actions. Analyse the variability in your data, particularly in biological assays, to understand the level of noise and its impact on model performance.</p> <ol start="9"> <li>Data redundancy and duplication checks</li> </ol> <p>Implement robust mechanisms for detecting and removing duplicate records, to prevent skewing of the data. Then, techniques such as correlation analysis can be used to identify and eliminate redundant features that do not contribute new information.</p> <ol start="10"> <li>Data imbalance handling</li> </ol> <p>Continuously monitor the balance of different classes in your data (eg, active versus inactive compounds). Address imbalances through methods such as over-sampling, under-sampling, or synthetic data generation. If data imbalance is unavoidable, consider using model algorithms that are better suited to handle imbalanced data.</p> <ol start="11"> <li>Data security and access control</li> </ol> <p>Security measures can be implemented to protect data from corruption, loss or unauthorised access, ensuring that data integrity is maintained. With this, access to data can be restricted based on roles and responsibilities to prevent unauthorised modifications or data entry errors.</p> <ol start="12"> <li>Communication and collaboration</li> </ol> <p>Foster collaboration between data scientists, domain experts and IT professionals to ensure that data quality requirements are clearly understood and addressed. A feedback loop should be established, whereby issues identified by data scientists or model results are communicated back to the experimental team to refine data collection processes.</p> <ol start="13"> <li>Use of quality control samples</li> </ol> <p>Include quality control samples (eg, known standards or replicates) in experimental runs to monitor and ensure consistency in assay performance. Regularly analyse the results of quality control samples to identify any drifts or deviations in experimental conditions that could impact data quality.</p> <ol start="14"> <li>Data quality metrics and reporting</li> </ol> <p>Define specific data quality metrics such as accuracy, completeness, consistency, timeliness, and uniqueness. Use these metrics to evaluate and report on the quality of your data regularly.</p> <p>When reporting data quality metrics to stakeholders, ensure transparency to facilitate continuous improvement.</p> <ol start="15"> <li>Continuous improvement</li> </ol> <p>When data quality issues are identified, perform a root cause analysis to understand the underlying reasons and implement corrective actions. You should treat data quality improvement as an iterative process, continuously refining and enhancing your strategies as new challenges and technologies emerge.</p> <p>In the next article, which will be published Tuesday 24 September, we will present views from an industry veteran on the topic of data quality.</p> <div class="box"> <p><strong>About the author</strong></p> <p><strong><img data-lazyloaded="1" src="data:image/gif;base64,R0lGODdhAQABAPAAAMPDwwAAACwAAAAAAQABAAACAkQBADs=" decoding="async" class="alignleft wp-image-149134 size-thumbnail" data-src="https://www.drugtargetreview.com/wp-content/uploads/Raminderpal-Singh-120x120.jpg" alt="Raminderpal Singh" width="120" height="120" data-srcset="https://www.drugtargetreview.com/wp-content/uploads/Raminderpal-Singh-120x120.jpg 120w, https://www.drugtargetreview.com/wp-content/uploads/Raminderpal-Singh-150x150.jpg 150w" data-sizes="(max-width: 120px) 100vw, 120px" /><noscript><img decoding="async" class="alignleft wp-image-149134 size-thumbnail" src="https://www.drugtargetreview.com/wp-content/uploads/Raminderpal-Singh-120x120.jpg" alt="Raminderpal Singh" width="120" height="120" srcset="https://www.drugtargetreview.com/wp-content/uploads/Raminderpal-Singh-120x120.jpg 120w, https://www.drugtargetreview.com/wp-content/uploads/Raminderpal-Singh-150x150.jpg 150w" sizes="(max-width: 120px) 100vw, 120px" /></noscript>Dr Raminderpal Singh</strong></p> <p>Dr Raminderpal Singh is a recognised visionary in the implementation of AI across technology and science-focused industries. He has over 30 years of global experience leading and advising teams, helping early to mid-stage companies achieve breakthroughs through the effective use of computational modelling. </p> <p>Raminderpal is currently the Global Head of AI and GenAI Practice at 20/15 Visioneers. He also founded and leads the HitchhikersAI.org open-source community. He is also a co-founder of Incubate Bio – a techbio providing a service to life sciences companies who are looking to accelerate their research and lower their wet lab costs through <em>in silico</em> modelling. </p> <p>Raminderpal has extensive experience building businesses in both Europe and the US. As a business executive at IBM Research in New York, Dr Singh led the go-to-market for IBM Watson Genomics Analytics. He was also Vice President and Head of the Microbiome Division at Eagle Genomics Ltd, in Cambridge. Raminderpal earned his PhD in semiconductor modelling in 1997. He has published several papers and two books and has twelve issued patents. In 2003, he was selected by EE Times as one of the top 13 most influential people in the semiconductor industry.</p> <p>For more: <a href="http://raminderpalsingh.com" target="_blank" rel="noopener">http://raminderpalsingh.com</a>; <a href="http://20visioneers15.com" target="_blank" rel="noopener">http://20visioneers15.com</a>; <a href="http://hitchhikersai.org" target="_blank" rel="noopener">http://hitchhikersAI.org</a>; <a href="http://incubate.bio" target="_blank" rel="noopener">http://incubate.bio</a> </p> </div> <div id="taxos2"> <div class="terms"><p>Related topics<br /><a href="https://www.drugtargetreview.com/topic/artificial-intelligence/" rel="tag">Artificial Intelligence</a>, <a href="https://www.drugtargetreview.com/topic/targets/" rel="tag">Targets</a></p></div> <div class="terms"><p>Related organisations<br /><a href="https://www.drugtargetreview.com/organisations/20-15-visioneers/" rel="tag">20/15 Visioneers</a>, <a href="https://www.drugtargetreview.com/organisations/hitchhikersai/" rel="tag">HitchhikersAI</a></p></div> <div class="terms"><p>Related people<br /><a href="https://www.drugtargetreview.com/people/dr-raminderpal-singh-hitchhikers-ai-and-20-15-visioneers/" rel="tag">Dr Raminderpal Singh (Hitchhikers AI and 20/15 Visioneers)</a></p></div></div> </article>    <div id="sidebarFallback1" class="bodyMpu"> <div id="group3"> <div class="randomordercontent group3">  <div id="ad20"></div> <script data-cfasync="false" src="/cdn-cgi/scripts/5c5dd728/cloudflare-static/email-decode.min.js"></script><script type="text/javascript"> jQuery(document).ready(function() { var cur_width = jQuery(document).width(); console.log("Screen width: " + cur_width); if ( cur_width < 780 ) { console.log("Showing show-if-lt in ad20 (167548)"); scrsize_load_url("ad20", 167548, 203200, 300, 250); } }); </script> </div> <div class="randomordercontent group3">  <div id="ad21"></div> <script type="text/javascript"> 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