Li Liao

Associate Professor of Computer and Information Sciences
Affiliated Faculty, Delaware Biotechnology Institute
Ph.D., Peking University (1992)

F08 Office Hours: 2:00PM-3:00PM, Tuesdays and Thursdays

Office: 424 Smith Hall

Postal Address
University of Delaware
103 Smith Hall
Newark, DE 19716

Phone: 302-831-3500
Fax: 302-831-8458

• ISIBM09 ISIBM International Joint Conferences on Bioinformatics, Systems Biology and Intelligent Computing, August 3-6, 2009, Shanghai, China.
• BIBM08 IEEE Bioinformatics and Biomedicine, November 3-5, 2008, Philadelphia, PA, USA.
• ICMLA08 The Seventh International Conference on Machine Learning and Applications, 11-13 Dec. 2008 San Diego, California, USA.
• BioKDD08 The 8th International Workshop on Data Mining in Bioinformatics, Las Vegas, NV, USA, August 24, 2008.
• BMEI'08 The International Conference on BioMedical Engineering and Informatics (BMEI2008), May 2008, Sanya, Hainan, China.
• IIBM'08 2008 International Workshop on Intelligent Informatics in Biology and Medicine March 4th – 7th, 2008, Polytechnic University of Catalonia, Barcelona, Spain

• Graduate fellowships are available via IGERT

Teaching

• CISC 841 Bioinformatics (F08)
• SIG Bioinformatics Seminar Series

  List of courses taught before.

Research (Liao Research Lab)

My research interests are in bioinformatics. I am particularly interested in developing statistical and machine learning methods that are effective, expressive and interpretable, via incorporating domain specific knowledge of biological systems.

The projects include:


1. Homology identification, protein family classification, and clustering. (Supported by DuPont Science and Engineering Award)
2. Molecular sequence modelling and analysis. (Supported by the NIH COBRE)
3. Comparative genomics and metabolic pathway evolution.
4. Pattern analysis for RNAi. (Supported by UDRF)
5. Sequence assembly of 454 and SBS data. (Supported by NSF)

Servers developed:

• TMModel: Transmembrane protein identification and topology prediction.
• QC-COMP: Quasi consensus comparison of profile Hidden Markov models for protein families.

Publications