Research

Our lab is interested in developing methods for the inference of gene regulatory mechanism and prediction of biomarkers based on genetic, genomic and epigenetic and clinical data. Our approaches are diverse, including machine learning, datamining, statistical modeling, and optimization methods.

Recently, we expanded our research interest to the area of microbiome studies. We are developing methods and tools (released MetaLonDA, WEVOTE, and PopPhy-CNN) for metagenomics data analysis and modeling host-microbiome interaction based on the use of combinations of modeling techniques mentioned above.