Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Pavlidis, Paul, Gillis, Jesse
Formato: Online Artículo Texto
Lenguaje:English
Publicado: F1000Research 2013
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
_version_ 1782308372427571200
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