Professor Victor Solovyev

Research interests

structures to discover non-random patterns responsible for their functions: prediction of promoters and splice sites using discriminant analysis, finding bacterial and eukaryotic genes by Hidden Markov Models, fast comparison of genomes, prediction of protein sub-cellular localization using neural networks approaches; fold recognition, modeling protein structure and protein-ligand docking; statistical analysis of expression data with finding disease specific genes; discovery and finding transcription regulation motifs and regulatory networks, developing databases of genomic information, building software pipelines of high-throughput computational analysis of bio-medical data; developing software to support new sequencing technologies (Solexa sequencing machine), SNP detection and transcriptome analysis (with RNASeq data). Application of bioinformatics approaches to potential drug targets discovery, modeling drug response and genetic networks gaining insights into the static and dynamic behavior of complex biological systems.

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