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Progress and challenges in the computational prediction of gene function using networks: 2012-2013 update
In an opinion published in 2012, we reviewed and discussed our studies of how gene network-based guilt-by-association (GBA) is impacted by confounds related to gene multifunctionality. We found such confounds account for a significant part of the GBA signal, and as a result meaningfully evaluating a...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
F1000Research
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3962002/ https://www.ncbi.nlm.nih.gov/pubmed/24715959 http://dx.doi.org/10.12688/f1000research.2-230.v1 |
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author | Pavlidis, Paul Gillis, Jesse |
author_facet | Pavlidis, Paul Gillis, Jesse |
author_sort | Pavlidis, Paul |
collection | PubMed |
description | In an opinion published in 2012, we reviewed and discussed our studies of how gene network-based guilt-by-association (GBA) is impacted by confounds related to gene multifunctionality. We found such confounds account for a significant part of the GBA signal, and as a result meaningfully evaluating and applying computationally-guided GBA is more challenging than generally appreciated. We proposed that effort currently spent on incrementally improving algorithms would be better spent in identifying the features of data that do yield novel functional insights. We also suggested that part of the problem is the reliance by computational biologists on gold standard annotations such as the Gene Ontology. In the year since, there has been continued heavy activity in GBA-based research, including work that contributes to our understanding of the issues we raised. Here we provide a review of some of the most relevant recent work, or which point to new areas of progress and challenges. |
format | Online Article Text |
id | pubmed-3962002 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | F1000Research |
record_format | MEDLINE/PubMed |
spelling | pubmed-39620022014-04-07 Progress and challenges in the computational prediction of gene function using networks: 2012-2013 update Pavlidis, Paul Gillis, Jesse F1000Res Opinion Article In an opinion published in 2012, we reviewed and discussed our studies of how gene network-based guilt-by-association (GBA) is impacted by confounds related to gene multifunctionality. We found such confounds account for a significant part of the GBA signal, and as a result meaningfully evaluating and applying computationally-guided GBA is more challenging than generally appreciated. We proposed that effort currently spent on incrementally improving algorithms would be better spent in identifying the features of data that do yield novel functional insights. We also suggested that part of the problem is the reliance by computational biologists on gold standard annotations such as the Gene Ontology. In the year since, there has been continued heavy activity in GBA-based research, including work that contributes to our understanding of the issues we raised. Here we provide a review of some of the most relevant recent work, or which point to new areas of progress and challenges. F1000Research 2013-10-31 /pmc/articles/PMC3962002/ /pubmed/24715959 http://dx.doi.org/10.12688/f1000research.2-230.v1 Text en Copyright: © 2013 Pavlidis P and Gillis J http://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. http://creativecommons.org/publicdomain/zero/1.0/ Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication). |
spellingShingle | Opinion Article Pavlidis, Paul Gillis, Jesse Progress and challenges in the computational prediction of gene function using networks: 2012-2013 update |
title | Progress and challenges in the computational prediction of gene function using networks: 2012-2013 update |
title_full | Progress and challenges in the computational prediction of gene function using networks: 2012-2013 update |
title_fullStr | Progress and challenges in the computational prediction of gene function using networks: 2012-2013 update |
title_full_unstemmed | Progress and challenges in the computational prediction of gene function using networks: 2012-2013 update |
title_short | Progress and challenges in the computational prediction of gene function using networks: 2012-2013 update |
title_sort | progress and challenges in the computational prediction of gene function using networks: 2012-2013 update |
topic | Opinion Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3962002/ https://www.ncbi.nlm.nih.gov/pubmed/24715959 http://dx.doi.org/10.12688/f1000research.2-230.v1 |
work_keys_str_mv | AT pavlidispaul progressandchallengesinthecomputationalpredictionofgenefunctionusingnetworks20122013update AT gillisjesse progressandchallengesinthecomputationalpredictionofgenefunctionusingnetworks20122013update |