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We develop novel computational methods based on machine learning, graph mining, and statistical methods and utilize them to address various real-world problems in biology and medicine. One of our major goals is to identify molecular mechanisms responsible for the variations in different phenotypic outcomes by analyzing the single-cell and bulk ‘omics’ (e.g. genomics, epigenomics, transcriptomics, etc.) profiles of samples while incorporating the known interactions of different molecules (e.g. proteins). We are particularly interested in identifying biomarkers and regulatory mechanisms involved in different traits of cancer such as metastasis and drug resistance.