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Community Detection in Large-Scale Bipartite Biological Networks

Networks are useful tools to represent and analyze interactions on a large, or genome-wide scale and have therefore been widely used in biology. Many biological networks—such as those that represent regulatory interactions, drug-gene, or gene-disease associations—are of a bipartite nature, meaning t...

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Autores principales: Calderer, Genís, Kuijjer, Marieke L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8099108/
https://www.ncbi.nlm.nih.gov/pubmed/33968132
http://dx.doi.org/10.3389/fgene.2021.649440
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author Calderer, Genís
Kuijjer, Marieke L.
author_facet Calderer, Genís
Kuijjer, Marieke L.
author_sort Calderer, Genís
collection PubMed
description Networks are useful tools to represent and analyze interactions on a large, or genome-wide scale and have therefore been widely used in biology. Many biological networks—such as those that represent regulatory interactions, drug-gene, or gene-disease associations—are of a bipartite nature, meaning they consist of two different types of nodes, with connections only forming between the different node sets. Analysis of such networks requires methodologies that are specifically designed to handle their bipartite nature. Community structure detection is a method used to identify clusters of nodes in a network. This approach is especially helpful in large-scale biological network analysis, as it can find structure in networks that often resemble a “hairball” of interactions in visualizations. Often, the communities identified in biological networks are enriched for specific biological processes and thus allow one to assign drugs, regulatory molecules, or diseases to such processes. In addition, comparison of community structures between different biological conditions can help to identify how network rewiring may lead to tissue development or disease, for example. In this mini review, we give a theoretical basis of different methods that can be applied to detect communities in bipartite biological networks. We introduce and discuss different scores that can be used to assess the quality of these community structures. We then apply a wide range of methods to a drug-gene interaction network to highlight the strengths and weaknesses of these methods in their application to large-scale, bipartite biological networks.
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spelling pubmed-80991082021-05-06 Community Detection in Large-Scale Bipartite Biological Networks Calderer, Genís Kuijjer, Marieke L. Front Genet Genetics Networks are useful tools to represent and analyze interactions on a large, or genome-wide scale and have therefore been widely used in biology. Many biological networks—such as those that represent regulatory interactions, drug-gene, or gene-disease associations—are of a bipartite nature, meaning they consist of two different types of nodes, with connections only forming between the different node sets. Analysis of such networks requires methodologies that are specifically designed to handle their bipartite nature. Community structure detection is a method used to identify clusters of nodes in a network. This approach is especially helpful in large-scale biological network analysis, as it can find structure in networks that often resemble a “hairball” of interactions in visualizations. Often, the communities identified in biological networks are enriched for specific biological processes and thus allow one to assign drugs, regulatory molecules, or diseases to such processes. In addition, comparison of community structures between different biological conditions can help to identify how network rewiring may lead to tissue development or disease, for example. In this mini review, we give a theoretical basis of different methods that can be applied to detect communities in bipartite biological networks. We introduce and discuss different scores that can be used to assess the quality of these community structures. We then apply a wide range of methods to a drug-gene interaction network to highlight the strengths and weaknesses of these methods in their application to large-scale, bipartite biological networks. Frontiers Media S.A. 2021-04-21 /pmc/articles/PMC8099108/ /pubmed/33968132 http://dx.doi.org/10.3389/fgene.2021.649440 Text en Copyright © 2021 Calderer and Kuijjer. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Calderer, Genís
Kuijjer, Marieke L.
Community Detection in Large-Scale Bipartite Biological Networks
title Community Detection in Large-Scale Bipartite Biological Networks
title_full Community Detection in Large-Scale Bipartite Biological Networks
title_fullStr Community Detection in Large-Scale Bipartite Biological Networks
title_full_unstemmed Community Detection in Large-Scale Bipartite Biological Networks
title_short Community Detection in Large-Scale Bipartite Biological Networks
title_sort community detection in large-scale bipartite biological networks
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8099108/
https://www.ncbi.nlm.nih.gov/pubmed/33968132
http://dx.doi.org/10.3389/fgene.2021.649440
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