network assessor: an automated method for quantitative assessment of a network’s potential for gene function prediction

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ID: 216509
2014
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Abstract
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.

Availability: The software is available in Java JAR format at http://pages.genemania.org/tools/.
Reference Key
emontojo2014frontiersnetwork Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Jason eMontojo;Khalid eZuberi;Quentin eShao;Gary eBader;Quaid eMorris
Journal chemical record (new york, ny)
Year 2014
DOI
10.3389/fgene.2014.00123
URL
http://journal.frontiersin.org/Journal/10.3389/fgene.2014.00123/full
Keywords
Machine learning function prediction network biology

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