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dc.contributor.authorLiany, Hertyen
dc.contributor.authorRajapakse, Jagath Chandanaen
dc.contributor.authorKaruturi, R. Krishna Murthyen
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.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.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 (, 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 ( 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.description.versionPublished versionen
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