Tel: +1 803-777-7304
Fax: +1 803-777-3767

Swearing Engineering Building 3A66
Department of Computer Science and Engineering
University of South Carolina
Columiba, SC, 29201

Welcome to Machine Learning and Evolution Laboratory (MLEG) at the Department of Computer Science and Engineering, University of South Carolina. Our research focuses on development of machine learning, data mining, and evolutionary algorithms for knowledge discovery and innovation in bioinformatics, science, and engineering. We have worked on DNA regulatory motif discovery, microarray analysis, phenotype prediction/computational disease diagnosis, and gene/protein function prediction.

Currently, we are working on the protein targeting motif analysis and protein localization prediction problem. A typical protein has a size of only 10um while it contains about 1 billion proteins. How these proteins are targeted to their functional location within or outside of the cell is not well understood. It is known that most proteins carry a "postal code" or "address tag" like signals in the amino acid sequences. A complete understanding of these signals will greatly help to understand how the cells are assembled. We are also interested in developping algorithms for computational synthesis of mechtronics systems using genetic programming and bond graphs.

Other bioinformatics labs in the department Compuational biology Lab Dr. Tang's Lab. or visit our

Postdoc position, Ph.D. Research Assistantships are Availalbe. check out detail
The USC Material Doping Database (MDB) is online (Oct 25, 2013).

New software Online LR Ensemble server for protein localization prediction. (May 11, 2012).

Lewis Cawthorne won the University Magellan Scholarship on developing text mining system for protein sorting motif analysis.

Jhih-Rong, Kevin, Adel, Mythri joined the MLEG lab, Welcome! ( Spring, 2010).

Dr. Hu was awarded NSF Career Award for CAREER: Computational Analysis and Prediction of Genome-Wide Protein Targeting Signals and Localization.

New Bioinformatics Server AAEnrich for Physichemical property enrichment test for protein sorting motifs.