Selected publications:

J Zhang, SC Kou, JS Liu. Biopolymer structure simulation and optimization via fragment regrowth Monte Carlo. Journal of Chemical Physics, 126, 225101, (2007).

J Zhang, M Lin, R Chen, J Liang, JS Liu. Monte Carlo sampling of near-native structures of proteins and applications, Proteins, 66, 61-68, (2007)

J Zhang, JS Liu. On side-chain conformational entropy of proteins. PLoS Computational Biology, 2(12): e168. doi:10.1371/journal.pcbi.0020168, (2006)

Protein structure analysis and protein function prediction - the structure-function relationship of proteins

Proteins, the machines of life, are responsible in most biological processes. Proteins functions are determined by their structures. We try to understand the structure-function relationship of proteins through computational approaches.

Selected publication:

W Liu, A Srivastava, J Zhang. A mathematical framework for protein structure comparison. PLoS Computational Biology 7(2), (2011): e1001075. doi:10.1371/journal.pcbi.1001075.

Biological text mining and knowledge discovery

Traditionally, biomedical knowledge is stored in scientific publications. Reading published papers, reviews and books is the way to grasp what has been discovered and known in a particular scientific field. As scientific information is accumulating, their storage and organization become critical in terms of the retrieval, analysis and automatic computation of such information.

Selected publication and database/webserver:

Lindsey Bell, Rajesh Chowdhary, Jun S Liu, Xufeng Niu, Jinfeng Zhang. Integrated bio-entity network: a system for biological knowledge discovery. PLoS ONE, 2011, 6(6): e21474, doi:10.1371/journal.pone.0021474.

R Chowdhary, J Zhang, JS Liu. Bayesian Inference of Protein-protein Interactions from Biological Literature , Bioinformatics, 25(12), 1536-1542 (2009).

Protein-protein interaction extraction server: BioTextMiner.com

An integrated molecular interaction database: IntegrativeBiology.org

Statistical Genomics and its application in cancer research

High-throuput experiments are generating a large volume of data that are advancing biomedical science in unprecedented ways. We develop statistical and computational methods to analyze the high-throughput genomics data to address frontier problems in biology. One application of the tools is to study the gene regulatory networks in cancer.

Selected publications:

Tyrone Ryba, Ichiro Hiratani, Dana Battaglia, Micheal Kulik, Jinfeng Zhang, Stephen Dalton, and David M Gilbert. Replication timing: a fingerprint for cell identity and pluripotency, PLoS Computational Biology, 2011, 7(10): e1002225.