Research Focus

Informatics and Systems Biology of Complex Disease. The computational challenges of studying complex diseases, such as cancer, neurological disorders, diabetes, and deadly infections, are diverse and range from gathering biomedical data to determining the key molecular mechanisms behind the disease, from understanding the dynamics of the molecular system during the disease onset to making accurate predictions on the clinical outcomes. Our research in this area integrates the next-generation sequencing, structural genomics, and interactomics data and leverages state-of-the-art computational paradigms, such as deep learning, semi-supervised learning and agent-based systems. We are also working in collaboration with experimental labs to study specific diseases in human, animals, and plants.

Biomedical Data Analytics. We are developing computational methods for fast collection and processing of the large-scale biomedical datasets. Current projects include mining macromolecular interaction and genetic variation data and organizing it into a comprehensive database, text mining of host-pathogen interaction data using automated and crowd-sourcing approaches and mining eye-tracking movements to test basic cognitive hypothesis about learning.

Computational Genomics. Advances in computational genomics have the potential to greatly benefit evolutionary and regulatory genomics. We employ algorithms driven by biological phenomena to determine important functional and structural elements of the genome and trace their evolutionary origins. Current projects include discovering and analyzing genomic regions of extreme conservation in eukaryotic genomes and studying genome rearrangement in higher eukaryotes.