Professor in the Department of Biostatistics
Professor of Statistics, Department of Statistics, Harvard University
A wealth of biological sequence data and microarray expression data has emerged from the human genome project and functional genomics studies. In silico methods for understanding these data and for incorporating different sources of biological information/knowledge are becoming increasingly important. Because the nature is inherently stochastic, the essence of much of the computational efforts is statistical data analysis and probabilistic modeling. Dr. Liu is currently interested in the following topics: (a) predicting gene regulatory binding motifs; (b) homology modeling and sequence-based protein analysis; (c) linkage disequilibrium studies; and (d) phylogenetic studies.
Dr. Liu and his collaborators started to explore the utility of the statistical missing data formulation and Gibbs sampling strategies for biological sequence analysis since 1993. Over the years, they have developed a number of algorithms, including the Gibbs motif sampler, PROBE, Bayes Aligner, BioProspector, and MDscan, suitable for detecting subtle sequence relationships and weak repetitive patterns. In particular, the Gibbs motif sampler has been adopted by many other research groups and become a standard tool for finding DNA regulatory binding motifs. Dr. Liu?s long-term goals in computational gene regulation analysis are to develop strategies to efficiently combine sequence information, cross-species comparisons, and microarray analysis; to design new statistical models for eukaryotic gene regulation modules; to investigate the use of Bayesian network in understanding gene regulation.
Besides bioinformatics, Dr. Liu is also keenly interested in the general Monte Carlo methods (e.g., sequential Monte Carlo, Markov chain Monte Carlo, importance sampling) for integration and optimization in complex systems, and statistical applications in finance and engineering.
Ph.D., 1991, The University of Chicago