title: Network Assessor: an automated method for quantitative assessment of a network's potential for gene function prediction.
abstract: Significant effort has been invested in network-based gene function prediction algorithms based on the guilt by association (GBA) principle. Existing approaches for assessing prediction performance typically compute evaluation metrics, either averaged across all functions being considered, or strictly from properties of the network. Since the success of GBA algorithms depends on the specific function being predicted, evaluation metrics should instead be computed for each function. We describe a novel method for computing the usefulness of a network by measuring its impact on gene function cross validation prediction performance across all gene functions. We have implemented this in software called Network Assessor, and describe its use in the GeneMANIA (GM) quality control system. Network Assessor is part of the GM command line tools.
authors.affiliation: Donnelly Centre for Cellular and Biomolecular Research, University of Toronto Toronto, ON, Canada.
authors.forename: Jason
authors.initials: J
authors.lastname: Montojo
authors.affiliation: Donnelly Centre for Cellular and Biomolecular Research, University of Toronto Toronto, ON, Canada.
authors.forename: Khalid
authors.initials: K
authors.lastname: Zuberi
authors.affiliation: Donnelly Centre for Cellular and Biomolecular Research, University of Toronto Toronto, ON, Canada.
authors.forename: Quentin
authors.initials: Q
authors.lastname: Shao
authors.affiliation: Donnelly Centre for Cellular and Biomolecular Research, University of Toronto Toronto, ON, Canada.
authors.forename: Gary D
authors.initials: GD
authors.lastname: Bader
authors.affiliation: Donnelly Centre for Cellular and Biomolecular Research, University of Toronto Toronto, ON, Canada.
authors.forename: Quaid
authors.initials: Q
authors.lastname: Morris
keywords: cross validation; function prediction; machine learning; network biology; network inference