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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/2405.09595">arXiv:2405.09595</a> <span> [<a href="https://arxiv.org/pdf/2405.09595">pdf</a>, <a href="https://arxiv.org/format/2405.09595">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Other Quantitative Biology">q-bio.OT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Simplicity within biological complexity </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Przulj%2C+N">Natasa Przulj</a>, <a href="/search/q-bio?searchtype=author&query=Malod-Dognin%2C+N">Noel Malod-Dognin</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="2405.09595v1-abstract-short" style="display: inline;"> Heterogeneous, interconnected, systems-level, molecular data have become increasingly available and key in precision medicine. We need to utilize them to better stratify patients into risk groups, discover new biomarkers and targets, repurpose known and discover new drugs to personalize medical treatment. Existing methodologies are limited and a paradigm shift is needed to achieve quantitative and… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.09595v1-abstract-full').style.display = 'inline'; document.getElementById('2405.09595v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.09595v1-abstract-full" style="display: none;"> Heterogeneous, interconnected, systems-level, molecular data have become increasingly available and key in precision medicine. We need to utilize them to better stratify patients into risk groups, discover new biomarkers and targets, repurpose known and discover new drugs to personalize medical treatment. Existing methodologies are limited and a paradigm shift is needed to achieve quantitative and qualitative breakthroughs. In this perspective paper, we survey the literature and argue for the development of a comprehensive, general framework for embedding of multi-scale molecular network data that would enable their explainable exploitation in precision medicine in linear time. Network embedding methods map nodes to points in low-dimensional space, so that proximity in the learned space reflects the network's topology-function relationships. They have recently achieved unprecedented performance on hard problems of utilizing few omic data in various biomedical applications. However, research thus far has been limited to special variants of the problems and data, with the performance depending on the underlying topology-function network biology hypotheses, the biomedical applications and evaluation metrics. The availability of multi-omic data, modern graph embedding paradigms and compute power call for a creation and training of efficient, explainable and controllable models, having no potentially dangerous, unexpected behaviour, that make a qualitative breakthrough. We propose to develop a general, comprehensive embedding framework for multi-omic network data, from models to efficient and scalable software implementation, and to apply it to biomedical informatics. It will lead to a paradigm shift in computational and biomedical understanding of data and diseases that will open up ways to solving some of the major bottlenecks in precision medicine and other domains. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.09595v1-abstract-full').style.display = 'none'; document.getElementById('2405.09595v1-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> 15 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </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">29 pages, 4 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2; J.3 </p> </li> <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/2305.15453">arXiv:2305.15453</a> <span> [<a href="https://arxiv.org/pdf/2305.15453">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> </div> </div> <p class="title is-5 mathjax"> Drugst.One -- A plug-and-play solution for online systems medicine and network-based drug repurposing </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Maier%2C+A">Andreas Maier</a>, <a href="/search/q-bio?searchtype=author&query=Hartung%2C+M">Michael Hartung</a>, <a href="/search/q-bio?searchtype=author&query=Abovsky%2C+M">Mark Abovsky</a>, <a href="/search/q-bio?searchtype=author&query=Adamowicz%2C+K">Klaudia Adamowicz</a>, <a href="/search/q-bio?searchtype=author&query=Bader%2C+G+D">Gary D. Bader</a>, <a href="/search/q-bio?searchtype=author&query=Baier%2C+S">Sylvie Baier</a>, <a href="/search/q-bio?searchtype=author&query=Blumenthal%2C+D+B">David B. Blumenthal</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">Jing Chen</a>, <a href="/search/q-bio?searchtype=author&query=Elkjaer%2C+M+L">Maria L. Elkjaer</a>, <a href="/search/q-bio?searchtype=author&query=Garcia-Hernandez%2C+C">Carlos Garcia-Hernandez</a>, <a href="/search/q-bio?searchtype=author&query=Helmy%2C+M">Mohamed Helmy</a>, <a href="/search/q-bio?searchtype=author&query=Hoffmann%2C+M">Markus Hoffmann</a>, <a href="/search/q-bio?searchtype=author&query=Jurisica%2C+I">Igor Jurisica</a>, <a href="/search/q-bio?searchtype=author&query=Kotlyar%2C+M">Max Kotlyar</a>, <a href="/search/q-bio?searchtype=author&query=Lazareva%2C+O">Olga Lazareva</a>, <a href="/search/q-bio?searchtype=author&query=Levi%2C+H">Hagai Levi</a>, <a href="/search/q-bio?searchtype=author&query=List%2C+M">Markus List</a>, <a href="/search/q-bio?searchtype=author&query=Lobentanzer%2C+S">Sebastian Lobentanzer</a>, <a href="/search/q-bio?searchtype=author&query=Loscalzo%2C+J">Joseph Loscalzo</a>, <a href="/search/q-bio?searchtype=author&query=Malod-Dognin%2C+N">Noel Malod-Dognin</a>, <a href="/search/q-bio?searchtype=author&query=Manz%2C+Q">Quirin Manz</a>, <a href="/search/q-bio?searchtype=author&query=Matschinske%2C+J">Julian Matschinske</a>, <a href="/search/q-bio?searchtype=author&query=Mee%2C+M">Miles Mee</a>, <a href="/search/q-bio?searchtype=author&query=Oubounyt%2C+M">Mhaned Oubounyt</a>, <a href="/search/q-bio?searchtype=author&query=Pico%2C+A+R">Alexander R. Pico</a> , et al. (14 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="2305.15453v2-abstract-short" style="display: inline;"> In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood. In response, computational systems and network medicine tools have emerged to identify potential drug repurposing candidates. However, these tools often require complex installation and lack intuitive visual network mining… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.15453v2-abstract-full').style.display = 'inline'; document.getElementById('2305.15453v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2305.15453v2-abstract-full" style="display: none;"> In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood. In response, computational systems and network medicine tools have emerged to identify potential drug repurposing candidates. However, these tools often require complex installation and lack intuitive visual network mining capabilities. To tackle these challenges, we introduce Drugst.One, a platform that assists specialized computational medicine tools in becoming user-friendly, web-based utilities for drug repurposing. With just three lines of code, Drugst.One turns any systems biology software into an interactive web tool for modeling and analyzing complex protein-drug-disease networks. Demonstrating its broad adaptability, Drugst.One has been successfully integrated with 21 computational systems medicine tools. Available at https://drugst.one, Drugst.One has significant potential for streamlining the drug discovery process, allowing researchers to focus on essential aspects of pharmaceutical treatment research. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.15453v2-abstract-full').style.display = 'none'; document.getElementById('2305.15453v2-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> 4 July, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 24 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 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">45 pages, 6 figures, 7 tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2007.01107">arXiv:2007.01107</a> <span> [<a href="https://arxiv.org/pdf/2007.01107">pdf</a>, <a href="https://arxiv.org/format/2007.01107">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> </div> </div> <p class="title is-5 mathjax"> Integrative Data Analytic Framework to Enhance Cancer Precision Medicine </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Gaudelet%2C+T">Thomas Gaudelet</a>, <a href="/search/q-bio?searchtype=author&query=Malod-Dognin%2C+N">Noel Malod-Dognin</a>, <a href="/search/q-bio?searchtype=author&query=Przulj%2C+N">Natasa Przulj</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="2007.01107v1-abstract-short" style="display: inline;"> With the advancement of high-throughput biotechnologies, we increasingly accumulate biomedical data about diseases, especially cancer. There is a need for computational models and methods to sift through, integrate, and extract new knowledge from the diverse available data to improve the mechanistic understanding of diseases and patient care. To uncover molecular mechanisms and drug indications fo… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2007.01107v1-abstract-full').style.display = 'inline'; document.getElementById('2007.01107v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2007.01107v1-abstract-full" style="display: none;"> With the advancement of high-throughput biotechnologies, we increasingly accumulate biomedical data about diseases, especially cancer. There is a need for computational models and methods to sift through, integrate, and extract new knowledge from the diverse available data to improve the mechanistic understanding of diseases and patient care. To uncover molecular mechanisms and drug indications for specific cancer types, we develop an integrative framework able to harness a wide range of diverse molecular and pan-cancer data. We show that our approach outperforms competing methods and can identify new associations. Furthermore, through the joint integration of data sources, our framework can also uncover links between cancer types and molecular entities for which no prior knowledge is available. Our new framework is flexible and can be easily reformulated to study any biomedical problems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2007.01107v1-abstract-full').style.display = 'none'; document.getElementById('2007.01107v1-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> 2 July, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2020. </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">18 pages</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1901.10005">arXiv:1901.10005</a> <span> [<a href="https://arxiv.org/pdf/1901.10005">pdf</a>, <a href="https://arxiv.org/format/1901.10005">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</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.0231059">10.1371/journal.pone.0231059 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Unveiling new disease, pathway, and gene associations via multi-scale neural networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Gaudelet%2C+T">Thomas Gaudelet</a>, <a href="/search/q-bio?searchtype=author&query=Malod-Dognin%2C+N">Noel Malod-Dognin</a>, <a href="/search/q-bio?searchtype=author&query=Sanchez-Valle%2C+J">Jon Sanchez-Valle</a>, <a href="/search/q-bio?searchtype=author&query=Pancaldi%2C+V">Vera Pancaldi</a>, <a href="/search/q-bio?searchtype=author&query=Valencia%2C+A">Alfonso Valencia</a>, <a href="/search/q-bio?searchtype=author&query=Przulj%2C+N">Natasa Przulj</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="1901.10005v3-abstract-short" style="display: inline;"> Diseases involve complex processes and modifications to the cellular machinery. The gene expression profile of the affected cells contains characteristic patterns linked to a disease. Hence, biological knowledge pertaining to a disease can be derived from a patient cell's profile, improving our diagnosis ability, as well as our grasp of disease risks. This knowledge can be used for drug re-purposi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1901.10005v3-abstract-full').style.display = 'inline'; document.getElementById('1901.10005v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1901.10005v3-abstract-full" style="display: none;"> Diseases involve complex processes and modifications to the cellular machinery. The gene expression profile of the affected cells contains characteristic patterns linked to a disease. Hence, biological knowledge pertaining to a disease can be derived from a patient cell's profile, improving our diagnosis ability, as well as our grasp of disease risks. This knowledge can be used for drug re-purposing, or by physicians to evaluate a patient's condition and co-morbidity risk. Here, we look at differential gene expression obtained from microarray technology for patients diagnosed with various diseases. Based on this data and cellular multi-scale organization, we aim to uncover disease--disease links, as well as disease-gene and disease--pathways associations. We propose neural networks with structures inspired by the multi-scale organization of a cell. We show that these models are able to correctly predict the diagnosis for the majority of the patients. Through the analysis of the trained models, we predict and validate disease-disease, disease-pathway, and disease-gene associations with comparisons to known interactions and literature search, proposing putative explanations for the novel predictions that come from our study. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1901.10005v3-abstract-full').style.display = 'none'; document.getElementById('1901.10005v3-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> 10 April, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 28 January, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2019. </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">16 pages</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> PLOS ONE, 15(4), p.e0231059 (2020) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1804.05003">arXiv:1804.05003</a> <span> [<a href="https://arxiv.org/pdf/1804.05003">pdf</a>, <a href="https://arxiv.org/ps/1804.05003">ps</a>, <a href="https://arxiv.org/format/1804.05003">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/bioinformatics/bty570">10.1093/bioinformatics/bty570 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Higher order molecular organisation as a source of biological function </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Gaudelet%2C+T">Thomas Gaudelet</a>, <a href="/search/q-bio?searchtype=author&query=Malod-Dognin%2C+N">Noel Malod-Dognin</a>, <a href="/search/q-bio?searchtype=author&query=Przulj%2C+N">Natasa Przulj</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="1804.05003v2-abstract-short" style="display: inline;"> Molecular interactions have widely been modelled as networks. The local wiring patterns around molecules in molecular networks are linked with their biological functions. However, networks model only pairwise interactions between molecules and cannot explicitly and directly capture the higher order molecular organisation, such as protein complexes and pathways. Hence, we ask if hypergraphs (hypern… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1804.05003v2-abstract-full').style.display = 'inline'; document.getElementById('1804.05003v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1804.05003v2-abstract-full" style="display: none;"> Molecular interactions have widely been modelled as networks. The local wiring patterns around molecules in molecular networks are linked with their biological functions. However, networks model only pairwise interactions between molecules and cannot explicitly and directly capture the higher order molecular organisation, such as protein complexes and pathways. Hence, we ask if hypergraphs (hypernetworks), that directly capture entire complexes and pathways along with protein-protein interactions (PPIs), carry additional functional information beyond what can be uncovered from networks of pairwise molecular interactions. The mathematical formalism of a hypergraph has long been known, but not often used in studying molecular networks due to the lack of sophisticated algorithms for mining the underlying biological information hidden in the wiring patterns of molecular systems modelled as hypernetworks. We propose a new, multi-scale, protein interaction hypernetwork model that utilizes hypergraphs to capture different scales of protein organization, including PPIs, protein complexes and pathways. In analogy to graphlets, we introduce hypergraphlets, small, connected, non-isomorphic, induced sub-hypergraphs of a hypergraph, to quantify the local wiring patterns of these multi-scale molecular hypergraphs and to mine them for new biological information. We apply them to model the multi-scale protein networks of baker yeast and human and show that the higher order molecular organisation captured by these hypergraphs is strongly related to the underlying biology. Importantly, we demonstrate that our new models and data mining tools reveal different, but complementary biological information compared to classical PPI networks. We apply our hypergraphlets to successfully predict biological functions of uncharacterised proteins. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1804.05003v2-abstract-full').style.display = 'none'; document.getElementById('1804.05003v2-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> 20 September, 2018; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 13 April, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2018. </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">This article has been accepted for publication in Bioinformatics Published by Oxford University Press, https://academic.oup.com/bioinformatics/article/34/17/i944/5093209</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1804.04428">arXiv:1804.04428</a> <span> [<a href="https://arxiv.org/pdf/1804.04428">pdf</a>, <a href="https://arxiv.org/format/1804.04428">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> <p class="title is-5 mathjax"> Functional geometry of protein-protein interaction networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Malod-Dognin%2C+N">Noel Malod-Dognin</a>, <a href="/search/q-bio?searchtype=author&query=Przulj%2C+N">Natasa Przulj</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="1804.04428v1-abstract-short" style="display: inline;"> Motivation: Protein-protein interactions (PPIs) are usually modelled as networks. These networks have extensively been studied using graphlets, small induced subgraphs capturing the local wiring patterns around nodes in networks. They revealed that proteins involved in similar functions tend to be similarly wired. However, such simple models can only represent pairwise relationships and cannot ful… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1804.04428v1-abstract-full').style.display = 'inline'; document.getElementById('1804.04428v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1804.04428v1-abstract-full" style="display: none;"> Motivation: Protein-protein interactions (PPIs) are usually modelled as networks. These networks have extensively been studied using graphlets, small induced subgraphs capturing the local wiring patterns around nodes in networks. They revealed that proteins involved in similar functions tend to be similarly wired. However, such simple models can only represent pairwise relationships and cannot fully capture the higher-order organization of protein interactions, including protein complexes. Results: To model the multi-sale organization of these complex biological systems, we utilize simplicial complexes from computational geometry. The question is how to mine these new representations of PPI networks to reveal additional biological information. To address this, we define simplets, a generalization of graphlets to simplicial complexes. By using simplets, we define a sensitive measure of similarity between simplicial complex network representations that allows for clustering them according to their data types better than clustering them by using other state-of-the-art measures, e.g., spectral distance, or facet distribution distance. We model human and baker's yeast PPI networks as simplicial complexes that capture PPIs and protein complexes as simplices. On these models, we show that our newly introduced simplet-based methods cluster proteins by function better than the clustering methods that use the standard PPI networks, uncovering the new underlying functional organization of the cell. We demonstrate the existence of the functional geometry in the PPI data and the superiority of our simplet-based methods to effectively mine for new biological information hidden in the complexity of the higher order organization of PPI networks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1804.04428v1-abstract-full').style.display = 'none'; document.getElementById('1804.04428v1-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> 12 April, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2018. </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">16 pages, 11 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1410.7585">arXiv:1410.7585</a> <span> [<a href="https://arxiv.org/pdf/1410.7585">pdf</a>, <a href="https://arxiv.org/format/1410.7585">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> <p class="title is-5 mathjax"> FUSE: Multiple Network Alignment via Data Fusion </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Gligorijevi%C4%87%2C+V">Vladimir Gligorijevi膰</a>, <a href="/search/q-bio?searchtype=author&query=Malod-Dognin%2C+N">No毛l Malod-Dognin</a>, <a href="/search/q-bio?searchtype=author&query=Pr%C5%BEulj%2C+N">Nata拧a Pr啪ulj</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="1410.7585v2-abstract-short" style="display: inline;"> Discovering patterns in networks of protein-protein interactions (PPIs) is a central problem in systems biology. Alignments between these networks aid functional understanding as they uncover important information, such as evolutionary conserved pathways, protein complexes and functional orthologs. The objective of a multiple network alignment is to create clusters of nodes that are evolutionarily… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1410.7585v2-abstract-full').style.display = 'inline'; document.getElementById('1410.7585v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1410.7585v2-abstract-full" style="display: none;"> Discovering patterns in networks of protein-protein interactions (PPIs) is a central problem in systems biology. Alignments between these networks aid functional understanding as they uncover important information, such as evolutionary conserved pathways, protein complexes and functional orthologs. The objective of a multiple network alignment is to create clusters of nodes that are evolutionarily conserved and functionally consistent across all networks. Unfortunately, the alignment methods proposed thus far do not fully meet this objective, as they are guided by pairwise scores that do not utilize the entire functional and topological information across all networks. To overcome this weakness, we propose FUSE, a multiple network aligner that utilizes all functional and topological information in all PPI networks. It works in two steps. First, it computes novel similarity scores of proteins across the PPI networks by fusing from all aligned networks both the protein wiring patterns and their sequence similarities. It does this by using Non-negative Matrix Tri-Factorization (NMTF). When we apply NMTF on the five largest and most complete PPI networks from BioGRID, we show that NMTF finds a larger number of protein pairs across the PPI networks that are functionally conserved than can be found by using protein sequence similarities alone. This demonstrates complementarity of protein sequence and their wiring patterns in the PPI networks. In the second step, FUSE uses a novel maximum weight k-partite matching approximation algorithm to find an alignment between multiple networks. We compare FUSE with the state of the art multiple network aligners and show that it produces the largest number of functionally consistent clusters that cover all aligned PPI networks. Also, FUSE is more computationally efficient than other multiple network aligners. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1410.7585v2-abstract-full').style.display = 'none'; document.getElementById('1410.7585v2-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> 3 November, 2014; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 28 October, 2014; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2014. </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">15 pages, 3 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/0906.0125">arXiv:0906.0125</a> <span> [<a href="https://arxiv.org/pdf/0906.0125">pdf</a>, <a href="https://arxiv.org/ps/0906.0125">ps</a>, <a href="https://arxiv.org/format/0906.0125">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/10.2390/biecoll-jib-2010-120">10.2390/biecoll-jib-2010-120 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> An integrative approach to modeling biological networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Memisevic%2C+V">Vesna Memisevic</a>, <a href="/search/q-bio?searchtype=author&query=Milenkovic%2C+T">Tijana Milenkovic</a>, <a href="/search/q-bio?searchtype=author&query=Przulj%2C+N">Natasa Przulj</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="0906.0125v2-abstract-short" style="display: inline;"> Since proteins carry out biological processes by interacting with other proteins, analyzing the structure of protein-protein interaction (PPI) networks could explain complex biological mechanisms, evolution, and disease. Similarly, studying protein structure networks, residue interaction graphs (RIGs), might provide insights into protein folding, stability, and function. The first step towards u… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('0906.0125v2-abstract-full').style.display = 'inline'; document.getElementById('0906.0125v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="0906.0125v2-abstract-full" style="display: none;"> Since proteins carry out biological processes by interacting with other proteins, analyzing the structure of protein-protein interaction (PPI) networks could explain complex biological mechanisms, evolution, and disease. Similarly, studying protein structure networks, residue interaction graphs (RIGs), might provide insights into protein folding, stability, and function. The first step towards understanding these networks is finding an adequate network model that closely replicates their structure. Evaluating the fit of a model to the data requires comparing the model with real-world networks. Since network comparisons are computationally infeasible, they rely on heuristics, or "network properties." We show that it is difficult to assess the reliability of the fit of a model with any individual network property. Thus, our approach integrates a variety of network properties and further combines these with a series of probabilistic methods to predict an appropriate network model for biological networks. We find geometric random graphs, that model spatial relationships between objects, to be the best-fitting model for RIGs. This validates the correctness of our method, since RIGs have previously been shown to be geometric. We apply our approach to noisy PPI networks and demonstrate that their structure is also consistent with geometric random graphs. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('0906.0125v2-abstract-full').style.display = 'none'; document.getElementById('0906.0125v2-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> 24 September, 2009; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 30 May, 2009; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2009. </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">10 pages, 3 tables, 4 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Vesna Memisevic, Tijana Milenkovic and Natasa Przulj. An integrative approach to modeling biological networks. Journal of Integrative Bioinformatics, 7(3):120, 2010. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/0901.4589">arXiv:0901.4589</a> <span> [<a href="https://arxiv.org/pdf/0901.4589">pdf</a>, <a href="https://arxiv.org/ps/0901.4589">ps</a>, <a href="https://arxiv.org/format/0901.4589">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/bioinformatics/btl301">10.1093/bioinformatics/btl301 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Biological network comparison using graphlet degree distribution </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Przulj%2C+N">Natasa Przulj</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="0901.4589v1-abstract-short" style="display: inline;"> Analogous to biological sequence comparison, comparing cellular networks is an important problem that could provide insight into biological understanding and therapeutics. For technical reasons, comparing large networks is computationally infeasible, and thus heuristics such as the degree distribution have been sought. It is easy to demonstrate that two networks are different by simply showing a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('0901.4589v1-abstract-full').style.display = 'inline'; document.getElementById('0901.4589v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="0901.4589v1-abstract-full" style="display: none;"> Analogous to biological sequence comparison, comparing cellular networks is an important problem that could provide insight into biological understanding and therapeutics. For technical reasons, comparing large networks is computationally infeasible, and thus heuristics such as the degree distribution have been sought. It is easy to demonstrate that two networks are different by simply showing a short list of properties in which they differ. It is much harder to show that two networks are similar, as it requires demonstrating their similarity in all of their exponentially many properties. Clearly, it is computationally prohibitive to analyze all network properties, but the larger the number of constraints we impose in determining network similarity, the more likely it is that the networks will truly be similar. We introduce a new systematic measure of a network's local structure that imposes a large number of similarity constraints on networks being compared. In particular, we generalize the degree distribution, which measures the number of nodes 'touching' k edges, into distributions measuring the number of nodes 'touching' k graphlets, where graphlets are small connected non-isomorphic subgraphs of a large network. Our new measure of network local structure consists of 73 graphlet degree distributions (GDDs) of graphlets with 2-5 nodes, but it is easily extendible to a greater number of constraints (i.e. graphlets). Furthermore, we show a way to combine the 73 GDDs into a network 'agreement' measure. Based on this new network agreement measure, we show that almost all of the 14 eukaryotic PPI networks, including human, are better modeled by geometric random graphs than by Erdos-Reny, random scale-free, or Barabasi-Albert scale-free networks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('0901.4589v1-abstract-full').style.display = 'none'; document.getElementById('0901.4589v1-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 January, 2009; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2009. </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">Proceedings of the 2006 European Conference on Computational Biology, ECCB'06, Eilat, Israel, January 21-24, 2007</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Bioinformatics 2007 23: e177-e183 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/0810.3280">arXiv:0810.3280</a> <span> [<a href="https://arxiv.org/pdf/0810.3280">pdf</a>, <a href="https://arxiv.org/ps/0810.3280">ps</a>, <a href="https://arxiv.org/format/0810.3280">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> <p class="title is-5 mathjax"> Topological network alignment uncovers biological function and phylogeny </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Kuchaiev%2C+O">Oleksii Kuchaiev</a>, <a href="/search/q-bio?searchtype=author&query=Milenkovic%2C+T">Tijana Milenkovic</a>, <a href="/search/q-bio?searchtype=author&query=Memisevic%2C+V">Vesna Memisevic</a>, <a href="/search/q-bio?searchtype=author&query=Hayes%2C+W">Wayne Hayes</a>, <a href="/search/q-bio?searchtype=author&query=Przulj%2C+N">Natasa Przulj</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="0810.3280v4-abstract-short" style="display: inline;"> Sequence comparison and alignment has had an enormous impact on our understanding of evolution, biology, and disease. Comparison and alignment of biological networks will likely have a similar impact. Existing network alignments use information external to the networks, such as sequence, because no good algorithm for purely topological alignment has yet been devised. In this paper, we present a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('0810.3280v4-abstract-full').style.display = 'inline'; document.getElementById('0810.3280v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="0810.3280v4-abstract-full" style="display: none;"> Sequence comparison and alignment has had an enormous impact on our understanding of evolution, biology, and disease. Comparison and alignment of biological networks will likely have a similar impact. Existing network alignments use information external to the networks, such as sequence, because no good algorithm for purely topological alignment has yet been devised. In this paper, we present a novel algorithm based solely on network topology, that can be used to align any two networks. We apply it to biological networks to produce by far the most complete topological alignments of biological networks to date. We demonstrate that both species phylogeny and detailed biological function of individual proteins can be extracted from our alignments. Topology-based alignments have the potential to provide a completely new, independent source of phylogenetic information. Our alignment of the protein-protein interaction networks of two very different species--yeast and human--indicate that even distant species share a surprising amount of network topology with each other, suggesting broad similarities in internal cellular wiring across all life on Earth. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('0810.3280v4-abstract-full').style.display = 'none'; document.getElementById('0810.3280v4-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> 7 October, 2009; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 17 October, 2008; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2008. </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">Algorithm explained in more details. Additional analysis added</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/0802.0556">arXiv:0802.0556</a> <span> [<a href="https://arxiv.org/pdf/0802.0556">pdf</a>, <a href="https://arxiv.org/ps/0802.0556">ps</a>, <a href="https://arxiv.org/format/0802.0556">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> <p class="title is-5 mathjax"> Uncovering Biological Network Function via Graphlet Degree Signatures </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Milenkovic%2C+T">Tijana Milenkovic</a>, <a href="/search/q-bio?searchtype=author&query=Przulj%2C+N">Natasa Przulj</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="0802.0556v1-abstract-short" style="display: inline;"> Proteins are essential macromolecules of life and thus understanding their function is of great importance. The number of functionally unclassified proteins is large even for simple and well studied organisms such as baker's yeast. Methods for determining protein function have shifted their focus from targeting specific proteins based solely on sequence homology to analyses of the entire proteom… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('0802.0556v1-abstract-full').style.display = 'inline'; document.getElementById('0802.0556v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="0802.0556v1-abstract-full" style="display: none;"> Proteins are essential macromolecules of life and thus understanding their function is of great importance. The number of functionally unclassified proteins is large even for simple and well studied organisms such as baker's yeast. Methods for determining protein function have shifted their focus from targeting specific proteins based solely on sequence homology to analyses of the entire proteome based on protein-protein interaction (PPI) networks. Since proteins aggregate to perform a certain function, analyzing structural properties of PPI networks may provide useful clues about the biological function of individual proteins, protein complexes they participate in, and even larger subcellular machines. We design a sensitive graph theoretic method for comparing local structures of node neighborhoods that demonstrates that in PPI networks, biological function of a node and its local network structure are closely related. The method groups topologically similar proteins under this measure in a PPI network and shows that these protein groups belong to the same protein complexes, perform the same biological functions, are localized in the same subcellular compartments, and have the same tissue expressions. Moreover, we apply our technique on a proteome-scale network data and infer biological function of yet unclassified proteins demonstrating that our method can provide valuable guidelines for future experimental research. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('0802.0556v1-abstract-full').style.display = 'none'; document.getElementById('0802.0556v1-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> 5 February, 2008; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2008. </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">First submitted to Nature Biotechnology on July 16, 2007. Presented at BioPathways'07 pre-conference of ISMB/ECCB'07, July 19-20, 2007, Vienna, Austria. Published in full in the Posters section of the Schedule of the RECOMB Satellite Conference on Systems Biology, November 30 - December 1, 2007, University of California, San Diego, USA</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> Technical Report No. 08-01 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/q-bio/0404017">arXiv:q-bio/0404017</a> <span> [<a href="https://arxiv.org/pdf/q-bio/0404017">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> </div> </div> <p class="title is-5 mathjax"> Modeling Interactome: Scale-Free or Geometric? </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Przulj%2C+N">Natasa Przulj</a>, <a href="/search/q-bio?searchtype=author&query=Corneil%2C+D+G">Derek G. Corneil</a>, <a href="/search/q-bio?searchtype=author&query=Jurisica%2C+I">Igor Jurisica</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="q-bio/0404017v1-abstract-short" style="display: inline;"> Networks have been used to model many real-world phenomena to better understand the phenomena and to guide experiments in order to predict their behavior. Since incorrect models lead to incorrect predictions, it is vital to have a correct model. As a result, new techniques and models for analyzing and modeling real-world networks have recently been introduced. One example of large and complex ne… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('q-bio/0404017v1-abstract-full').style.display = 'inline'; document.getElementById('q-bio/0404017v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="q-bio/0404017v1-abstract-full" style="display: none;"> Networks have been used to model many real-world phenomena to better understand the phenomena and to guide experiments in order to predict their behavior. Since incorrect models lead to incorrect predictions, it is vital to have a correct model. As a result, new techniques and models for analyzing and modeling real-world networks have recently been introduced. One example of large and complex networks involves protein-protein interaction (PPI) networks. We demonstrate that the currently popular scale-free model of PPI networks fails to fit the data in several respects. We show that a random geometric model provides a much more accurate model of the PPI data. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('q-bio/0404017v1-abstract-full').style.display = 'none'; document.getElementById('q-bio/0404017v1-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> 16 April, 2004; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2004. </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">53 pages, 18 figures, 5 tables</span> </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 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