With background in biology and Ph.D. degree in plant genetics, Andrey Alexeyenko has focused his research interests on understanding normal physiology and pathology with gene network and systems biology approaches. It implies development of computational infrastructure, i.e. tools that integrate diverse high-throughput data across technological platforms and species. The set of tools and methods includes FunCoup: a computational framework for reconstructing gene networks via systematic integration of heterogeneous datasets http://funcoup2.sbc.su.se/and EviNet: a web suit for network enrichment analysis https://www.evinet.org.
Network enrichment analysis; pathway enrichment; global interaction networks; network science
NEArender: an R package for functional interpretation of 'omics' data via network enrichment analysis.
BMC Bioinformatics 2017 Mar;18(Suppl 5):118
RhoA knockout fibroblasts lose tumor-inhibitory capacity in vitro and promote tumor growth in vivo.
Proc. Natl. Acad. Sci. U.S.A. 2017 02;114(8):E1413-E1421
Confrontation of fibroblasts with cancer cells in vitro: gene network analysis of transcriptome changes and differential capacity to inhibit tumor growth.
J. Exp. Clin. Cancer Res. 2015 Jun;34():62
Distinguishing between driver and passenger mutations in individual cancer genomes by network enrichment analysis.
BMC Bioinformatics 2014 Sep;15():308
Network enrichment analysis: extension of gene-set enrichment analysis to gene networks.
BMC Bioinformatics 2012 Sep;13():226
Dynamic zebrafish interactome reveals transcriptional mechanisms of dioxin toxicity.
PLoS ONE 2010 May;5(5):e10465
Automatic clustering of orthologs and inparalogs shared by multiple proteomes.
Bioinformatics 2006 Jul;22(14):e9-15