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is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2309.08478">arXiv:2309.08478</a> <span> [<a href="https://arxiv.org/pdf/2309.08478">pdf</a>, <a href="https://arxiv.org/format/2309.08478">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Molecular Networks">q-bio.MN</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1093/bioadv/vbae099">10.1093/bioadv/vbae099 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Current and future directions in network biology </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Zitnik%2C+M">Marinka Zitnik</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+M+M">Michelle M. Li</a>, <a href="/search/q-bio?searchtype=author&query=Wells%2C+A">Aydin Wells</a>, <a href="/search/q-bio?searchtype=author&query=Glass%2C+K">Kimberly Glass</a>, <a href="/search/q-bio?searchtype=author&query=Gysi%2C+D+M">Deisy Morselli Gysi</a>, <a href="/search/q-bio?searchtype=author&query=Krishnan%2C+A">Arjun Krishnan</a>, <a href="/search/q-bio?searchtype=author&query=Murali%2C+T+M">T. M. Murali</a>, <a href="/search/q-bio?searchtype=author&query=Radivojac%2C+P">Predrag Radivojac</a>, <a href="/search/q-bio?searchtype=author&query=Roy%2C+S">Sushmita Roy</a>, <a href="/search/q-bio?searchtype=author&query=Baudot%2C+A">Ana茂s Baudot</a>, <a href="/search/q-bio?searchtype=author&query=Bozdag%2C+S">Serdar Bozdag</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+D+Z">Danny Z. Chen</a>, <a href="/search/q-bio?searchtype=author&query=Cowen%2C+L">Lenore Cowen</a>, <a href="/search/q-bio?searchtype=author&query=Devkota%2C+K">Kapil Devkota</a>, <a href="/search/q-bio?searchtype=author&query=Gitter%2C+A">Anthony Gitter</a>, <a href="/search/q-bio?searchtype=author&query=Gosline%2C+S">Sara Gosline</a>, <a href="/search/q-bio?searchtype=author&query=Gu%2C+P">Pengfei Gu</a>, <a href="/search/q-bio?searchtype=author&query=Guzzi%2C+P+H">Pietro H. Guzzi</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+H">Heng Huang</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+M">Meng Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Kesimoglu%2C+Z+N">Ziynet Nesibe Kesimoglu</a>, <a href="/search/q-bio?searchtype=author&query=Koyuturk%2C+M">Mehmet Koyuturk</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+J">Jian Ma</a>, <a href="/search/q-bio?searchtype=author&query=Pico%2C+A+R">Alexander R. Pico</a>, <a href="/search/q-bio?searchtype=author&query=Pr%C5%BEulj%2C+N">Nata拧a Pr啪ulj</a> , et al. (12 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2309.08478v2-abstract-short" style="display: inline;"> Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These challenges stem from various fa… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.08478v2-abstract-full').style.display = 'inline'; document.getElementById('2309.08478v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2309.08478v2-abstract-full" style="display: none;"> Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These challenges stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology and highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on the future directions of network biology. Additionally, we offer insights into scientific communities, educational initiatives, and the importance of fostering diversity within the field. This paper establishes a roadmap for an immediate and long-term vision for network biology. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.08478v2-abstract-full').style.display = 'none'; document.getElementById('2309.08478v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 15 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">52 pages, 6 figures, 1 table</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2211.14800">arXiv:2211.14800</a> <span> [<a href="https://arxiv.org/pdf/2211.14800">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Molecular Networks">q-bio.MN</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Applications">stat.AP</span> </div> </div> <p class="title is-5 mathjax"> Non-Coding RNAs Improve the Predictive Power of Network Medicine </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Gysi%2C+D+M">Deisy Morselli Gysi</a>, <a href="/search/q-bio?searchtype=author&query=Barabasi%2C+A">Albert-Laszlo Barabasi</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2211.14800v1-abstract-short" style="display: inline;"> Network Medicine has improved the mechanistic understanding of disease, offering quantitative insights into disease mechanisms, comorbidities, and novel diagnostic tools and therapeutic treatments. Yet, most network-based approaches rely on a comprehensive map of protein-protein interactions, ignoring interactions mediated by non-coding RNAs (ncRNAs). Here, we systematically combine experimentally… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.14800v1-abstract-full').style.display = 'inline'; document.getElementById('2211.14800v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2211.14800v1-abstract-full" style="display: none;"> Network Medicine has improved the mechanistic understanding of disease, offering quantitative insights into disease mechanisms, comorbidities, and novel diagnostic tools and therapeutic treatments. Yet, most network-based approaches rely on a comprehensive map of protein-protein interactions, ignoring interactions mediated by non-coding RNAs (ncRNAs). Here, we systematically combine experimentally confirmed binding interactions mediated by ncRNA with protein-protein interactions, constructing the first comprehensive network of all physical interactions in the human cell. We find that the inclusion of ncRNA, expands the number of genes in the interactome by 46% and the number of interactions by 107%, significantly enhancing our ability to identify disease modules. Indeed, we find that 132 diseases, lacked a statistically significant disease module in the protein-based interactome, but have a statistically significant disease module after inclusion of ncRNA-mediated interactions, making these diseases accessible to the tools of network medicine. We show that the inclusion of ncRNAs helps unveil disease-disease relationships that were not detectable before and expands our ability to predict comorbidity patterns between diseases. Taken together, we find that including non-coding interactions improves both the breath and the predictive accuracy of network medicine. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.14800v1-abstract-full').style.display = 'none'; document.getElementById('2211.14800v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 November, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Paper and SI</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2004.07229">arXiv:2004.07229</a> <span> [<a href="https://arxiv.org/pdf/2004.07229">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Molecular Networks">q-bio.MN</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1073/pnas.2025581118">10.1073/pnas.2025581118 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Network Medicine Framework for Identifying Drug Repurposing Opportunities for COVID-19 </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Gysi%2C+D+M">Deisy Morselli Gysi</a>, <a href="/search/q-bio?searchtype=author&query=Valle%2C+%C3%8D+D">脥talo Do Valle</a>, <a href="/search/q-bio?searchtype=author&query=Zitnik%2C+M">Marinka Zitnik</a>, <a href="/search/q-bio?searchtype=author&query=Ameli%2C+A">Asher Ameli</a>, <a href="/search/q-bio?searchtype=author&query=Gan%2C+X">Xiao Gan</a>, <a href="/search/q-bio?searchtype=author&query=Varol%2C+O">Onur Varol</a>, <a href="/search/q-bio?searchtype=author&query=Ghiassian%2C+S+D">Susan Dina Ghiassian</a>, <a href="/search/q-bio?searchtype=author&query=Patten%2C+J">JJ Patten</a>, <a href="/search/q-bio?searchtype=author&query=Davey%2C+R">Robert Davey</a>, <a href="/search/q-bio?searchtype=author&query=Loscalzo%2C+J">Joseph Loscalzo</a>, <a href="/search/q-bio?searchtype=author&query=Barab%C3%A1si%2C+A">Albert-L谩szl贸 Barab谩si</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2004.07229v2-abstract-short" style="display: inline;"> The current pandemic has highlighted the need for methodologies that can quickly and reliably prioritize clinically approved compounds for their potential effectiveness for SARS-CoV-2 infections. In the past decade, network medicine has developed and validated multiple predictive algorithms for drug repurposing, exploiting the sub-cellular network-based relationship between a drug's targets and di… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2004.07229v2-abstract-full').style.display = 'inline'; document.getElementById('2004.07229v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2004.07229v2-abstract-full" style="display: none;"> The current pandemic has highlighted the need for methodologies that can quickly and reliably prioritize clinically approved compounds for their potential effectiveness for SARS-CoV-2 infections. In the past decade, network medicine has developed and validated multiple predictive algorithms for drug repurposing, exploiting the sub-cellular network-based relationship between a drug's targets and disease genes. Here, we deployed algorithms relying on artificial intelligence, network diffusion, and network proximity, tasking each of them to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. To test the predictions, we used as ground truth 918 drugs that had been experimentally screened in VeroE6 cells, and the list of drugs under clinical trial, that capture the medical community's assessment of drugs with potential COVID-19 efficacy. We find that while most algorithms offer predictive power for these ground truth data, no single method offers consistently reliable outcomes across all datasets and metrics. This prompted us to develop a multimodal approach that fuses the predictions of all algorithms, showing that a consensus among the different predictive methods consistently exceeds the performance of the best individual pipelines. We find that 76 of the 77 drugs that successfully reduced viral infection do not bind the proteins targeted by SARS-CoV-2, indicating that these drugs rely on network-based actions that cannot be identified using docking-based strategies. These advances offer a methodological pathway to identify repurposable drugs for future pathogens and neglected diseases underserved by the costs and extended timeline of de novo drug development. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2004.07229v2-abstract-full').style.display = 'none'; document.getElementById('2004.07229v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 9 August, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 15 April, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1802.00828">arXiv:1802.00828</a> <span> [<a href="https://arxiv.org/pdf/1802.00828">pdf</a>, <a href="https://arxiv.org/format/1802.00828">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation">stat.CO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Molecular Networks">q-bio.MN</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Applications">stat.AP</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1371/journal.pone.0240523">10.1371/journal.pone.0240523 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Comparing multiple networks using the Co-expression Differential Network Analysis (CoDiNA) </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Gysi%2C+D+M">Deisy Morselli Gysi</a>, <a href="/search/q-bio?searchtype=author&query=Fragoso%2C+T+M">Tiago Miranda Fragoso</a>, <a href="/search/q-bio?searchtype=author&query=Buskamp%2C+V">Volker Buskamp</a>, <a href="/search/q-bio?searchtype=author&query=Almaas%2C+E">Eivind Almaas</a>, <a href="/search/q-bio?searchtype=author&query=Nowick%2C+K">Katja Nowick</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1802.00828v2-abstract-short" style="display: inline;"> Biomedical sciences are increasingly recognising the relevance of gene co-expression-networks for analysing complex-systems, phenotypes or diseases. When the goal is investigating complex-phenotypes under varying conditions, it comes naturally to employ comparative network methods. While approaches for comparing two networks exist, this is not the case for multiple networks. Here we present a meth… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1802.00828v2-abstract-full').style.display = 'inline'; document.getElementById('1802.00828v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1802.00828v2-abstract-full" style="display: none;"> Biomedical sciences are increasingly recognising the relevance of gene co-expression-networks for analysing complex-systems, phenotypes or diseases. When the goal is investigating complex-phenotypes under varying conditions, it comes naturally to employ comparative network methods. While approaches for comparing two networks exist, this is not the case for multiple networks. Here we present a method for the systematic comparison of an unlimited number of networks: Co-expression Differential Network Analysis (CoDiNA) for detecting links and nodes that are common, specific or different to the networks. Applying CoDiNA to a neurogenesis study identified genes for neuron differentiation. Experimentally overexpressing one candidate resulted in significant disturbance in the underlying neurogenesis' gene regulatory network. We compared data from adults and children with active tuberculosis to test for signatures of HIV. We also identified common and distinct network features for particular cancer types with CoDiNA. These studies show that CoDiNA successfully detects genes associated with the diseases. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1802.00828v2-abstract-full').style.display = 'none'; document.getElementById('1802.00828v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 September, 2018; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 2 February, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2018. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1711.04702">arXiv:1711.04702</a> <span> [<a href="https://arxiv.org/pdf/1711.04702">pdf</a>, <a href="https://arxiv.org/format/1711.04702">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Molecular Networks">q-bio.MN</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Applications">stat.AP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation">stat.CO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Methodology">stat.ME</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1186/s12859-018-2351-7">10.1186/s12859-018-2351-7 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> wTO: an R package for computing weighted topological overlap and consensus networks with an integrated visualization tool </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Gysi%2C+D+M">Deisy Morselli Gysi</a>, <a href="/search/q-bio?searchtype=author&query=Voigt%2C+A">Andre Voigt</a>, <a href="/search/q-bio?searchtype=author&query=Fragoso%2C+T+d+M">Tiago de Miranda Fragoso</a>, <a href="/search/q-bio?searchtype=author&query=Almaas%2C+E">Eivind Almaas</a>, <a href="/search/q-bio?searchtype=author&query=Nowick%2C+K">Katja Nowick</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1711.04702v2-abstract-short" style="display: inline;"> Network analyses, such as of gene co-expression networks, metabolic networks and ecological networks have become a central approach for the systems-level study of biological data. Several software packages exist for generating and analyzing such networks, either from correlation scores or the absolute value of a transformed score called weighted topological overlap (wTO). However, since gene regul… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1711.04702v2-abstract-full').style.display = 'inline'; document.getElementById('1711.04702v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1711.04702v2-abstract-full" style="display: none;"> Network analyses, such as of gene co-expression networks, metabolic networks and ecological networks have become a central approach for the systems-level study of biological data. Several software packages exist for generating and analyzing such networks, either from correlation scores or the absolute value of a transformed score called weighted topological overlap (wTO). However, since gene regulatory processes can up- or down-regulate genes, it is of great interest to explicitly consider both positive and negative correlations when constructing a gene co-expression network. Here, we present an R package for calculating the wTO, that, in contrast to existing packages, explicitly addresses the sign of the wTO values, and is thus especially valuable for the analysis of gene regulatory networks. The package includes the calculation of p-values (raw and adjusted) for each pairwise gene score. Our package also allows the calculation of networks from time series (without replicates). Since networks from independent datasets (biological repeats or related studies) are not the same due to technical and biological noise in the data, we additionally, incorporated a novel method for calculating a consensus network (CN) from two or more networks into our R package. We compare our new wTO package to state of art packages and demonstrate the application of the wTO and CN functions using 3 independently derived datasets from healthy human pre-frontal cortex samples. To showcase an example for the time series application we utilized a metagenomics data set. In this work, we developed a software package that allows the computation of wTO networks, CNs and a visualization tool in the R statistical environment. It is publicly available on CRAN repositories under the GPL-2 Open Source License (https://cran.r-project.org/web/packages/wTO/). <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1711.04702v2-abstract-full').style.display = 'none'; document.getElementById('1711.04702v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 September, 2018; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 13 November, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2017. </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" aria-label="Secondary">  <div 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