Network Assessor: An automated method for quantitative assessment of a network’s potential for gene function prediction

• Main Authors: Jason eMontojo, Khalid eZuberi, Quentin eShao, Gary eBader, Quaid eMorris
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2014-05-01
• Series: Frontiers in Genetics
• Subjects: machine learning; network biology; function prediction; Network Inference; cross validation
• Online Access: http://journal.frontiersin.org/Journal/10.3389/fgene.2014.00123/full

Networks of gene-gene interactions (or, functional interactions or more generally, associations) have proven very useful for predicting gene function. Association networks have proven useful in other biological domains to predict properties of nodes representing, e.g., patients, based on their connectivity with other nodes with pre-established properties. The quality of these predictions depends on the quality and relevance of the association data. For predicting gene function, there are hundreds of different networks that can be used and a plethora of different algorithms to use them—validating prediction performance can be time consuming and error prone. Here we describe methodology and software to automatically evaluate the contribution of an individual association network to predicting gene function (and more generally, predicting node function). This software is implemented in Network Assessor, which is part of the GeneMANIA command line tools. We also describe its use in the GeneMANIA quality control system.<br/><br/>Availability: The software is available in Java JAR format at http://pages.genemania.org/tools/.