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Dr LI, Shuaicheng 李帥成

BSc MSc NUS, PhD U of Waterloo

Associate Professor

Dr LI, Shuaicheng

Contact Information

Office: YEUNG-G6520 YEUNG
Phone: +(852)-3442-9412
Fax: +(852)-3442-0503
Email: shuaicli@cityu.edu.hk
Web: Personal Homepage

Research Interests

  • Bioinformatics
  • Machine Learning
  • Algorithms


(DBLP Bibliography Server | Google-Scholar  )

Journal Papers

  • Hexagon codes accurate information for side chain conformation
    Shuai Cheng Li, Dongbo Bu, and Ming Li
    IEEE/ACM Transactions on Computational Biology and Bioinformatics (IEEE TCBB), Accepted.
  • Finding Nearly Optimal GDT Scores
    Shuai Cheng Li, Dongbo Bu, Jinbo Xu and Ming Li
    Journal of Computational Biology (JCB), Accepted.
  • Protein Secondary Structure Prediction Using NMR Chemical Shift Data
    Yuzhong Zhao, Babak Alipanahi, Shuai Cheng Li, and Ming Li
    Journal of Bioinformatics and Computational Biology (JBCB) Vol. 8, No. 5, pages 867-884, 2010.
  • On Protein Structure Alignment under Distance Constraint
    Shuai Cheng Li and Yen Kaow Ng
    Theoretical Computer Science (TCS): Special Issue for ISAAC 2009. Vol. 412, No. 32, pages 4187-4199, 2011
  • Calibur: a tool for clustering large numbers of protein decoys
    Shuai Cheng Li and Yen Kaow Ng
    BMC Bioinformatics (BMCBI), Vol. 11, No. 25,
  • Finding compact structural motifs
    Dongbo Bu, Ming Li, Shuai Cheng Li, Jianbo Qian, Jinbo Xu
    Theoretical Computer Science (TCS) Vol. 410, No. 30-32, pages 2834-2839, 2009
  • On Two Open Problems in 2-interval Patterns
    Shuai Cheng Li, Ming Li
    Theoretical Computer Science (TCS) Vol. 410, No. 24-25, pages 2410-2423, 2009
  • Predicting local quality of a sequence-structure alignment
    Xin Gao, Jinbo Xu, Shuai Cheng Li, Ming Li
    Journal of Bioinformatics and Computational Biology (JBCB) Vol. 7, No. 5, pages 789-810, 2009.
  • Discriminative Learning for Protein Conformation Sampling.
    Feng Zhao, Shuai Cheng Li, Beckett W. Sterner, Jinbo Xu
    PROTEINS: Structure, Function and Bioinformatics, Vol. 73 No. 1, pages 228-240, 2008.
  • Designing succinct structural alphabets
    Shuai Cheng Li, Dongbo Bu, Xin Gao, Jinbo Xu, Ming Li
    Bioinformatics: Special Issue for ISMB 2008, Vol. 24, No. 13, pages 182-189, 2008
  • A PTAS for the k-Consensus Structures Problem Under Euclidean Squared Distance
    Shuai Cheng Li, Yen Kaow Ng, Louxin Zhang
    Algorithms, Vol. 1, No. 2, pages 43-51, 2008.
  • Piers: An Efficient Model for Similarity Search in DNA Sequence Databases
    Xia Cao, Shuai Cheng Li, Beng Chin Ooi, Anthony K. H. Tung
    SIGMOD Record, Vol. 33, No. 2, pages 39-44, 2004.

Conference/Workshop Papers

  • Pedigree Reconstruction using Identity by Descent
    Bonnie Kirkpatrick, Shuai Cheng Li, Richard M. Karp and Eran Halperin
    Research in Computational Molecular Biology - 15th Annual International Conference (RECOMB 2011). Accepted.
  • Finding Largest Well-Predicted Subset of Protein Structure Models
    Shuai Cheng Li, Dongbo Bu, Jinbo Xu, and Ming Li
    Combinatorial Pattern Matching, 19th Annual Symposium (CPM 2008)
  • FragQA: predicting local fragment quality of a sequence-structure alignment (Best Paper Award)
    Xin Gao, Dongbo Bu, Shuai Cheng Li, Jinbo Xu, and Ming Li
    The 18th International Conference on Genome Informatics (GIW 2007)
  • Finding Compact Structural Motifs
    Jianbo Qian, Shuai Cheng Li, Dongbo Bu, Ming Li, and Jinbo Xu
    Combinatorial Pattern Matching, 18th Annual Symposium (CPM 2007)
  • Faster Algorithms for Finding Missing Patterns
    Shuai Cheng Li
    Theory of Computing 2006, Proceedings of the Twelfth Computing: The Australasian Theory Symposium (CATS 2006)
  • On the Complexity of the Crossing Contact Map Pattern Matching Problem
    Shuai Cheng Li and Ming Li
    Algorithms in Bioinformatics, 6th International Workshop (WABI 2006)
  • Indexing DNA Sequences Using q-Grams (Best Paper Award)
    Xia Cao, Shuai Cheng Li, and Anthony K. H. Tung
    Database Systems for Advanced Applications, 10th International Conference (DASFAA 2005)
  • New Approximation Algorithms for Some Dynamic Storage Allocation Problems
    Shuai Cheng Li, Hon Wai Leong, Steven K. Quek
    Computing and Combinatorics, 10th Annual International Conference (COCOON 2004)
  • String Join Using Precedence Count Matrix
    Xia Cao, Shuai Cheng Li, Beng Chin Ooi, Anthony K. H. Tung
    The 16th International Conference on Scientific and Statistical Database Management (SSDBM 2004)

System Demonstration Papers

  • Fragment-HMM: A New Approach To Protein Structure Prediction
    Shuai Cheng Li, Dongbo Bu, Jinbo Xu, Ming Li
    Protein Science, Vol. 17, No. 11, pages 1925-1934, 2008

Book Chapters

  • Consensus Approaches to Protein Structure Prediction
    Dongbo Bu, Shuai Cheng Li, Xin Gao, Libo Yu, Jinbo Xu and Ming Li
    Machine Learning in Bioinformatics, Edited by Yan-Qing Zhang and Jagath C. Rajapakse, John Wiley & Sons, ISBN 978-0-4701-1662-3, 2007.

Research Awards

  • NSERC Postdoctoral Fellowship (2009-2011)
    Natural Sciences and Engineering Research Council of Canada
    (Held at International Computer Science Institute, U Berkeley)
  • Outstanding Achievement in Graduate Studies Award (2010) 
    University of Waterloo, Canada
  • Cheriton Scholarship, University of Waterloo (2006 - 2009)
    University of Waterloo, Canada
  • International Student Scholarship, University of Waterloo (2004 - 2007)
    University of Waterloo, Canada
  • Best Paper Award (2007) 
    The 18th International Conference on Genome Informatics (GIW 2007)
  • Entrance Scholarship, University of Waterloo (2004 - 2005)
    University of Waterloo, Canada
  • Best Paper Award (2005) 
    10th International Conference on Database Systems for Advanced Applications (DASFAA 2005)
  • Research Scholarship, National University of Singapore (2001 - 2002)
    National University of Singapore, Singapore
  • First Runner-up, Algorithmic Programming Contest (2002)
    NUS-MIT Alliance Course on Algorithms (a course jointly conducted by NUS and MIT)
  • Dean's list, School of Computing, NUS (1997 - 1998)
    National University of Singapore, Singapore 

Selected Research Projects

    FALCON is a software system for protein structure prediction. It ranked among the top 3 protein structure prediction systems on hard targets in CASP8 (Assessment of Techniques for Protein Structure Prediction 8, http://robetta.bakerlab.org/CASP8_eval_domains/CASP8.FR_H.First-GDT_MM.html.) The system uses a simple position-specific hidden Markov model to predict protein structures. The new framework naturally repeats itself to converge to a final target, conglomerating fragment assembly, clustering, target selection, refinement, and consensus, all in one process. Our initial implementation of this system converged to within 6 Angstrom of the native structures for 100% decoys on all 6 standard benchmark proteins used to evaluate the state-of-the-art system called ROSETTA, which achieved only 14% to 94% on the same data. The qualities of the best decoys and the final decoys our system converged to were also notably better. Recently, we completed an automatic system for determining protein structures from NMR spectra. It usually takes well trained experts several months of experimentations to infer a structure from NMR data manually. Our system, AMR, completely automates this process, and reduces the time needed to infer high resolution structures from several months to one day. The system works in a three parts pipeline: peak picking, chemical shift assignment and structure generation. My work was on the structure generation part, which is an extension of FALCON to work with partial NMR constraints: to accept chemical shift information, tolerate errors and refine structures. Initial results show that our system managed to build high resolution structures that are comparable to those produced by human experts.
    FRAZOR utilizes a linear programming model for finding structural alphabet candidates for a target sequence. The 3D structure of a protein sequence can be assembled from substructures that correspond to small segments of the sequence. For each small sequence segment, there are only a few likely substructures. They are called the structural alphabet for the segment. Classical approaches such as ROSETTA used sequence profile and secondary structure information to predict structural alphabet. In contrast, we utilized more structural information, such as solvent accessibility and contact capacity, for finding structural alphabet. We used an integer linear programming technique to derive the best combination of these sequences and structural information. Using this additional information, we were able to generate significantly more accurate and succinct structural alphabets ? more than 50% improvement over the accuracies obtained previously by others. With these novel structural alphabets, we are able to construct more accurate protein structures than the state-of-the-art ab initio protein structure prediction programs such as ROSETTA. We are also able to reduce the Kolodny's library size by a factor of 8, at the same accuracy.