Welcome to Dr. Kuang's Research Group!

Our research focuses on developing machine learning algorithms to understand biological systems with emphasis on disease-related research problems. We apply kernel methods e. g. support vector machines (SVMs), graph-based learning algorithms, structured output learning algorithms, and various other statistical models such as hidden Markov models to analyze SNPs, gene expressions, genomic sequences and structures, and clinical records. Our projects span the following topics,

  • Mining clinical and genetic markers of human disease: we design graph-based learning algorithms for mining clinical and genetic markers of disease-related phenotypes.
  • Identifying malaria parasite proteins for drug design: we apply remote homology detection algorithms to detecting drug targets from unidentified proteins in malaria parasite species.
  • Analysis of protein functions and structures: we use machine learning algorithms to infer protein-protein interaction, protein structures and functions.