Please use this identifier to cite or link to this item: https://doi.org/10.1038/s41598-017-06020-6
Title: Benchmarking selected computational gene network growing tools in context of virus-host interactions
Authors: Taye, B
Vaz, C
Tanavde, V
Kuznetsov, V.A
Eisenhaber, F 
Sugrue, R.J
Maurer-Stroh, S 
Keywords: small interfering RNA
algorithm
benchmarking
biology
gene ontology
gene regulatory network
genetics
host pathogen interaction
human
metabolism
procedures
signal transduction
virus
Algorithms
Benchmarking
Computational Biology
Gene Ontology
Gene Regulatory Networks
Host-Pathogen Interactions
Humans
RNA, Small Interfering
Signal Transduction
Viruses
Issue Date: 2017
Publisher: Nature Publishing Group
Citation: Taye, B, Vaz, C, Tanavde, V, Kuznetsov, V.A, Eisenhaber, F, Sugrue, R.J, Maurer-Stroh, S (2017). Benchmarking selected computational gene network growing tools in context of virus-host interactions. Scientific Reports 7 (1) : 5805. ScholarBank@NUS Repository. https://doi.org/10.1038/s41598-017-06020-6
Abstract: Several available online tools provide network growing functions where an algorithm utilizing different data sources suggests additional genes/proteins that should connect an input gene set into functionally meaningful networks. Using the well-studied system of influenza host interactions, we compare the network growing function of two free tools GeneMANIA and STRING and the commercial IPA for their performance of recovering known influenza A virus host factors previously identified from siRNA screens. The result showed that given small (?30 genes) or medium (?150 genes) input sets all three network growing tools detect significantly more known host factors than random human genes with STRING overall performing strongest. Extending the networks with all the three tools significantly improved the detection of GO biological processes of known host factors compared to not growing networks. Interestingly, the rate of identification of true host factors using computational network growing is equal or better to doing another experimental siRNA screening study which could also be true and applied to other biological pathways/processes. © 2017 The Author(s).
Source Title: Scientific Reports
URI: https://scholarbank.nus.edu.sg/handle/10635/174408
ISSN: 2045-2322
DOI: 10.1038/s41598-017-06020-6
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