Verleyen, W., Ballouz, S., Gillis, J. (March 2015) Measuring the wisdom of the crowds in network-based gene function inference. Bioinformatics, 31 (5). pp. 745-752. ISSN 1367-4803
Abstract
MOTIVATION: Network-based gene function inference methods have proliferated in recent years, but measurable progress remains elusive. We wished to better explore performance trends by controlling data and algorithm implementation, with a particular focus on the performance of aggregate predictions. RESULTS: Hypothesizing that popular methods would perform well without hand-tuning, we used well-characterized algorithms to produce verifiably 'untweaked' results. We find that most state-of-the-art machine learning methods obtain 'gold standard' performance as measured in critical assessments in defined tasks. Across a broad range of tests, we see close alignment in algorithm performances after controlling for the underlying data being used. We find that algorithm aggregation provides only modest benefits, with a 17% increase in AUROC above the mean AUROC. In contrast, data aggregation gains are enormous with an 88% improvement in mean AUROC. Altogether, we find substantial evidence to support the view that additional algorithm development has little to offer for gene function prediction. Availability and Implementation: The supplementary information contains a description of the algorithms, the network data parsed from different biological data resources, and a guide to the source code (available at: http://gillislab.cshl.edu/supplements/) CONTACT: jgillis@cshl.edu.
| Item Type: | Paper |
|---|---|
| Subjects: | bioinformatics > computational biology bioinformatics > genomics and proteomics > genetics & nucleic acid processing > DNA, RNA structure, function, modification > genes, structure and function |
| CSHL Authors: | |
| Communities: | CSHL labs > Gillis Lab Stanley Institute for Cognitive Genomics |
| Depositing User: | Matt Covey |
| Date: | 1 March 2015 |
| Date Deposited: | 07 Nov 2014 17:01 |
| Last Modified: | 06 Nov 2015 20:05 |
| Related URLs: | |
| URI: | https://repository.cshl.edu/id/eprint/30898 |
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