The extraction of useful knowledge from voluminous data produced by high-throuput genomics presents a significant challenge to a scientific community. Efficient mining of this data for the needs of biomedical research critically depends on seamless integration of clinical, genomic and experimental information with prior knowledge about genotype-phenotype relationships accumulated in a plethora of publicly available databases. Furthermore, such experimental data should be accessible to a variety of algorithms and analytical pipelines that drive computational analysis and data mining. To address these challenges we are developing Lynx -- a scalable computational platform based on cross-cutting requirements from multiple scientific groups for data integration, management and analysis. The goal of Lynx is to provide an end-to-end support for the analytical needs of various translational projects.
My major scientific interest is the development of the approaches for representation and analysis of complex biological systems and how these approaches can be applied to the discovery of the molecular mechanisms contributing to complex heritable disorders.
Engagement Manager/Solutions Architect
Sr. Postdoctoral Researcher
Research Interests: Network Biology; Data Integration (Kernel-based data fusion, Probabilistic Graphical Models); Machine Learning; Comparative Genomics; Gene Expression and Regulation; Microbial Genomics and Metagenomics; Human Genetics and Cancer Genetics