Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/89215
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dc.contributor.authorLiany, Hertyen
dc.contributor.authorRajapakse, Jagath Chandanaen
dc.contributor.authorKaruturi, R. Krishna Murthyen
dc.date.accessioned2018-05-17T05:39:09Zen
dc.date.accessioned2019-12-06T17:20:24Z-
dc.date.available2018-05-17T05:39:09Zen
dc.date.available2019-12-06T17:20:24Z-
dc.date.issued2017en
dc.identifier.citationLiany, H., Rajapakse, J. C., & Karuturi, R. K. M. (2017). MultiDCoX: Multi-factor analysis of differential co-expression. BMC Bioinformatics, 18(S16), 111-124.en
dc.identifier.urihttps://hdl.handle.net/10356/89215-
dc.identifier.urihttp://hdl.handle.net/10220/44821en
dc.description.abstractBackground: Differential co-expression DCX signifies change in degree of co-expression of a set of genes among different biological conditions. It has been used to identify differential co-expression networks or interactomes. Many algorithms have been developed for single-factor differential co-expression analysis and applied in a variety of studies. However, in many studies, the samples are characterized by multiple factors such as genetic markers, clinical variables and treatments. No algorithm or methodology is available for multi-factor analysis of differential co-expression. Results: We developed a novel formulation and a computationally efficient greedy search algorithm called MultiDCoX to perform multi-factor differential co-expression analysis. Simulated data analysis demonstrates that the algorithm can effectively elicit differentially co-expressed (DCX) gene sets and quantify the influence of each factor on co-expression. MultiDCoX analysis of a breast cancer dataset identified interesting biologically meaningful differentially co-expressed (DCX) gene sets along with genetic and clinical factors that influenced the respective differential co-expression. Conclusions: MultiDCoX is a space and time efficient procedure to identify differentially co-expressed gene sets and successfully identify influence of individual factors on differential co-expression.en
dc.format.extent14 p.en
dc.language.isoenen
dc.relation.ispartofseriesBMC Bioinformaticsen
dc.rights© 2017 The Author(s). Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.en
dc.subjectDifferential Co-expressionen
dc.subjectGene Expressionen
dc.titleMultiDCoX: Multi-factor analysis of differential co-expressionen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen
dc.identifier.doihttp://dx.doi.org/10.1186/s12859-017-1963-7en
dc.description.versionPublished versionen
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