Web site: Cheng Lab Office address:
Department of Computer Science
Engineering Building West 109, University of Missouri Office phone:
(573) 882-7306 Fax:
Bioinformatics, Systems Biology, Machine Learning and Data Mining
Bioinformatics and Systems Biology Laboratory (BSBL)
The research in my group focuses on developing and applying statistical machine learning techniques to address the problems in molecular biology. Currently we are developing bioinformatics algorithms and tools for proteomics, systems biology, and genomics. We have active projects in protein structure prediction, protein interaction and docking, protein function prediction, biological sequence alignments, inference and simulation of biological networks, and machine learning ranking.
The main techniques we are using include neural networks, support vector machines, hidden Markov models, graphical models, kernel methods, clustering methods, graph algorithms, dynamic programming, differential equations, information theory, (Bayesian) statistical methods. The tools, web services, and datasets produced by our research are freely available here. Our automated tools for the prediction of protein tertiary structure, domain boundary, disorder region, and contact map were ranked among the best methods in the last Critical Assessment of Techniques for Protein Structure Prediction (CASP7), 2006.
- J. Cheng, Z. Wang, A.N. Tegge and J. Eickholt. Prediction of Global and Local Quality of CASP8 Models by MULTICOM series.Proteins, vol. 77, pp. 181-184, 2009.
- N. Tegge, Z. Wang, J. Eickholt, and J. Cheng. NNcon: Improved Protein Contact Map Prediction Using 2D-Recursive Neural Networks.Nucleic Acids Research , vol. 37, pp. w515-w518, 2009.
- Z. Wang, A. N. Tegge, and J. Cheng. Evaluating the Absolute Quality of a Single Protein Model Using Support Vector Machines and Structural Features. Proteins, vol. 75, no. 3, 638-647, 2009.
- J. Cheng. A Multi-Template Combination Algorithm for Protein Comparative Modeling.BMC Structural Biology.8:18, 2008.
- J. Cheng. DOMAC: An Accurate, Hybrid Protein Domain Prediction Server. Nucleic Acids Research, vol. 35, pp. w354-w356, 2007.
- J. Cheng and P. Baldi. Improved Residue Contact Prediction Using Support Vector Machines and a Large Feature Set. BMCBioinformatics. 8:113, 2007.
- J. Cheng and P. Baldi. A Machine Learning Information Retrieval Approach to Protein Fold Recognition. Bioinformatics, vol. 22, no. 12, pp. 1456-1463, 2006.
- J. Cheng, A. Randall, and P. Baldi. Prediction of Protein Stability Changes for Single-Site Mutations Using Support Vector Machines. Proteins: Structure, Function, Bioinformatics, vol. 62, no. 4, pp. 1125-1132, 2006.
- J. Cheng, H. Saigo, and P. Baldi. Large-Scale Prediction of Disulphide Bridges Using Kernel Methods, Two-Dimensional Recursive Neural Networks, and Weighted Graph Matching.Proteins: Structure, Function, Bioinformatics, vol 62, no. 3, pp. 617-629, 2006.