Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/89469
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dc.contributor.authorTaye, Biruhalemen
dc.contributor.authorVaz, Candidaen
dc.contributor.authorTanavde, Viveken
dc.contributor.authorKuznetsov, Vladimir A.en
dc.contributor.authorEisenhaber, Franken
dc.contributor.authorSugrue, Richard J.en
dc.contributor.authorMaurer-Stroh, Sebastianen
dc.date.accessioned2018-06-06T08:36:24Zen
dc.date.accessioned2019-12-06T17:26:12Z-
dc.date.available2018-06-06T08:36:24Zen
dc.date.available2019-12-06T17:26:12Z-
dc.date.issued2017en
dc.identifier.citationTaye, B., Vaz, C., Tanavde, V., Kuznetsov, V. A., Eisenhaber, F., Sugrue, R. J., et al. (2017). Benchmarking selected computational gene network growing tools in context of virus-host interactions. Scientific Reports, 7(1), 5805-.en
dc.identifier.issn2045-2322en
dc.identifier.urihttps://hdl.handle.net/10356/89469-
dc.description.abstractSeveral 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.en
dc.description.sponsorshipASTAR (Agency for Sci., Tech. and Research, S’pore)en
dc.format.extent11 p.en
dc.language.isoenen
dc.relation.ispartofseriesScientific Reportsen
dc.rights© 2017 The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.en
dc.subjectNetwork Growing Toolsen
dc.subjectSiRNA Screening Studyen
dc.titleBenchmarking selected computational gene network growing tools in context of virus-host interactionsen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen
dc.contributor.schoolSchool of Biological Sciencesen
dc.identifier.doi10.1038/s41598-017-06020-6en
dc.description.versionPublished versionen
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Appears in Collections:SBS Journal Articles

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