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 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.
Centre for High-Throughput Biology and Department of Psychiatry, University of British Columbia, Vancouver, V6T1Z4, Canada.
Paul
P
Pavlidis
Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Woodbury, NY, 11797, USA.
Jesse
J
Gillis