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The power of protein interaction networks for associating genes with diseases
Motivation: Understanding the association between genetic diseases and their causal genes is an important problem concerning human health. With the recent influx of high-throughput data describing interactions between gene products, scientists have been provided a new avenue through which these asso...
Autores principales: | , |
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Formato: | Texto |
Lenguaje: | English |
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Oxford University Press
2010
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2853684/ https://www.ncbi.nlm.nih.gov/pubmed/20185403 http://dx.doi.org/10.1093/bioinformatics/btq076 |
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author | Navlakha, Saket Kingsford, Carl |
author_facet | Navlakha, Saket Kingsford, Carl |
author_sort | Navlakha, Saket |
collection | PubMed |
description | Motivation: Understanding the association between genetic diseases and their causal genes is an important problem concerning human health. With the recent influx of high-throughput data describing interactions between gene products, scientists have been provided a new avenue through which these associations can be inferred. Despite the recent interest in this problem, however, there is little understanding of the relative benefits and drawbacks underlying the proposed techniques. Results: We assessed the utility of physical protein interactions for determining gene–disease associations by examining the performance of seven recently developed computational methods (plus several of their variants). We found that random-walk approaches individually outperform clustering and neighborhood approaches, although most methods make predictions not made by any other method. We show how combining these methods into a consensus method yields Pareto optimal performance. We also quantified how a diffuse topological distribution of disease-related proteins negatively affects prediction quality and are thus able to identify diseases especially amenable to network-based predictions and others for which additional information sources are absolutely required. Availability: The predictions made by each algorithm considered are available online at http://www.cbcb.umd.edu/DiseaseNet Contact: carlk@cs.umd.edu Supplementary information: Supplementary data are available at Bioinformatics online. |
format | Text |
id | pubmed-2853684 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-28536842010-04-14 The power of protein interaction networks for associating genes with diseases Navlakha, Saket Kingsford, Carl Bioinformatics Original Papers Motivation: Understanding the association between genetic diseases and their causal genes is an important problem concerning human health. With the recent influx of high-throughput data describing interactions between gene products, scientists have been provided a new avenue through which these associations can be inferred. Despite the recent interest in this problem, however, there is little understanding of the relative benefits and drawbacks underlying the proposed techniques. Results: We assessed the utility of physical protein interactions for determining gene–disease associations by examining the performance of seven recently developed computational methods (plus several of their variants). We found that random-walk approaches individually outperform clustering and neighborhood approaches, although most methods make predictions not made by any other method. We show how combining these methods into a consensus method yields Pareto optimal performance. We also quantified how a diffuse topological distribution of disease-related proteins negatively affects prediction quality and are thus able to identify diseases especially amenable to network-based predictions and others for which additional information sources are absolutely required. Availability: The predictions made by each algorithm considered are available online at http://www.cbcb.umd.edu/DiseaseNet Contact: carlk@cs.umd.edu Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2010-04-15 2010-02-24 /pmc/articles/PMC2853684/ /pubmed/20185403 http://dx.doi.org/10.1093/bioinformatics/btq076 Text en © The Author(s) 2010. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/2.0/uk/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Papers Navlakha, Saket Kingsford, Carl The power of protein interaction networks for associating genes with diseases |
title | The power of protein interaction networks for associating genes with diseases |
title_full | The power of protein interaction networks for associating genes with diseases |
title_fullStr | The power of protein interaction networks for associating genes with diseases |
title_full_unstemmed | The power of protein interaction networks for associating genes with diseases |
title_short | The power of protein interaction networks for associating genes with diseases |
title_sort | power of protein interaction networks for associating genes with diseases |
topic | Original Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2853684/ https://www.ncbi.nlm.nih.gov/pubmed/20185403 http://dx.doi.org/10.1093/bioinformatics/btq076 |
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