Ting Chen
Department of Biological Sciences

University of Southern California
Los Angeles, CA 90089-2910 USA

Research Interests

  • Bioinformatics and Computational Biology
  • Computational Proteomics
  • Algorithm Analysis
  • Machine Learning

 

Education

  • Ph.D. (1997), Computer Science Department, SUNY at Stony Brook.
  • B.E. (1993), Department of Computer Science and Technology, Tsinghua University, Beijing.

 

Academic Experience

  • Associate Professor (2006-), Departments of Biology, Computer Science and Mathematics, USC.
  • Assistant Professor (2000-2006), Departments of Biology, Computer Science and Mathematics, USC.
  • Lecturer (1997-2000), Department of Genetics, Harvard Medical School.
  • Guest Research Associate (1995-1998), Biology Department, Brookhaven National Laboratory.

 

Awards and Grants

  • Alfred P. Sloan Research Fellowship, 2004.
  • Grant NIH R01 LM008991-01, Efficient Analysis of SNPs and Haplotypes with Applications in Gene Mapping. With Jing Li (PI) and Tao Jiang, 12/2005-12/2008.
  • Grant NIH RO1 AG024297-01, Redox proteomics of age-related neurodegeneration. With Austin Yang (PI), 9/2004-8/2009.
  • Grant NIH Center of Excellence in Genomic Sciences. PI: Michael Waterman, 9/2003-9/2008.
  • Grant NSF Joint DMS/NIGMS Initiative, Computational and Mathematical Study in Protein Interactions and Functions, With Fengzhu Sun (PI), 7/2003-7/2007.
  • Grant NIH NIGMS, Computational Mass Spectrometry, With Pavel Pevzner (PI, UCSD), 9/2002-9/2005.
  • Grant NSF-ITR, Algorithmic study in computational proteomics, 9/2001-9/2004.
  • Grant Undergraduate Research, Interdisciplinary Research in Drug Discovery: Proteomics and Bioinformatics, With Nouri Neamati, 2003-2004.
  • Grant Zumberge Fund, Tandem mass spectrometry, 7/2002-7/2003.

 

Research Activities

  • Editorial Board, BMC Bioinformatics.
  • Editorial Board, Applied Mathematics Research Express (AMRX).
  • Editorial Board, Expert Review of Proteomics.
  • Organizing Committee, Proteomics Workshops, IPAM UCLA, March-June, 2004.
  • Organizer, Computational Proteomics Workshops, MBI, OSU, Jan, 2005.
  • Program Committee, RECOMB2007, ISMB 2006, RECOMB2005, RECOMB 2004, RECOMB 2003
  • Program Committee, COCOON 2002

 

Publications

Mass Spectrometry

  1. Mo L, Dutta D, Wan Y and Chen T. MSNovo: A new dynamic programming algorithm for de novo peptide sequencing. Analytical Chemistry 2007 Jul 1;79(13):4870-8. (pdf)
  2. Dutta D. and Chen T. Speeding up Tandem Mass Spectrometry Database Search: Metric Embeddings and Fast Near Neighbor Search. Bioinformatics 2007; Mar 1;23(5):612-8. (pdf)
  3. Wan Y and Chen T. PepHMM: A hidden Markov model based scoring function for tandem mass spectrometry. Analytical Chemistry. 2006 Jan 15;78(2):432-7. (pdf)
  4. BA Soreghan, BW Lu, SN Thomas, K Duff, EA Rakhmatulin, T Nikolskaya, T Chen, & AJ. Yang (2005) Redox proteomic analysis of a PS1+APP mouse model of Alzheimer’s disease. The Journal of Alzheimer’s Disease. 2005 Dec;8(3):227-41.
  5. Lu, B. and Chen, T. Algorithms for de novo peptide sequencing via tandem mass spectrometry. Drug Discovery Today: BioSilico 2: 85-90, 2004. (pdf)
  6. Lu, B. and Chen, T. A Suffix Tree Approach to the Interpretation of Tandem Mass Spectra: Applications to Peptides of Nonspecific Digestion and Post-translational Modifications. Bioinformatics Suppl. 2 (ECCB), Page 113-121, 2003. (pdf)
  7. Lu, B. and Chen, T. A Suboptimal Algorithm for De novo Peptide Sequencing via Tandem Mass Spectrometry. Journal of Computational Biology, 10(1):1-12, 2003. (pdf)
  8. Chen, T., Jaffe, J. and Church, G.M. Algorithms for Identifying Protein Cross-links via Tandem Mass Spectrometry, Journal of Computational Biology, 8(6):571-583, 2001. (pdf)
  9. Chen, T., Kao, M.Y., Tepel, M., Rush, J., and Church, G.M. A Dynamic Programming Approach to De Novo Peptide Sequencing via Tandem Mass Spectrometry. Journal of Computational Biology, 8(3): 325-337, 2001. (pdf)
  10. Chen, T. Gene-Finding via Tandem Mass Spectrometry. The ACM-SIGACT Fifth Annual International Conference on Computational Molecular Biology (RECOMB01), Page 85-92, 2001. (pdf)

 

SNPs and Haplotypes

  1. Jiang R, Yang H, Kuo J CC, Sun F and Chen T. Sequence-based prioritization of nonsynonymous single nucleotide polymorphisms for the study of disease mutations. American Journal of Human Genetics. 2007 Aug;81(2):346-60. (pdf)
  2. Su, S, Kuo J CC, Chen T. Signal Processing Techniques for SNP (Single Nucleotide Polymorphism) Data Analysis. IEEE Signal Processing Magzine: Special Issue on Signal Processing Methods in Genomics and Proteomics. 2007. (pdf)
  3. Jiang R, Yang H, Sun F and Chen T. Searching for interpretable rules for disease mutations: A simulated annealing bump hunting strategy. BMC Bioinformatics. 2006 Sep 19;7(1):417. (pdf)
  4. Huang YT, Chao KM, and Chen T. An approximation algorithm for haplotype inference by maximum parsimony. Journal of Computational Biology, Dec 2005;12(10):1261-74. (pdf)
  5. Huang YT, Zhang K, Chen T and Chao KM. Approximation algorithms for the selection of robust tag SNPs. BMC Bioinformatics, 2005, 6:263 (pdf)
  6. Su S, Kuo CC and Chen T. Inference of Missing SNPs and Haplotype Block Partitioning. Bioinformatics, 2005 May 1;21(9):2001-7. (pdf)
  7. Zhang, K., Qin, Z., Chen, T., Liu, J., Waterman, MS, and Sun, F. HapBlock: Haplotype Block Partitioning and Tag SNP Selection Software Using a Set of Dynamic Programming Algorithms. Bioinformatics, 2005 Jan 1;21(1):131-4. (pdf)
  8. Zhang, K., Qin, Z., Liu, J., Chen, T., Waterman, MS, and Sun, F. Haplotype Block Partitioning and Tag SNP Selection Using Genotype Data and Their Applications to Association Studies. Genome Res. 2004 May;14(5): 908-16. (pdf)
  9. Zhang, K., Sun, F., Waterman, MS and Chen, T., Dynamic programming algorithms for partitioning sequence variation in human chromosomes. HERMIS 4:15-26, 2003.
  10. Zhang, K., Sun, F., Waterman, MS, Chen, T. Haplotype block partition with limited resources and applications to human chromosome 21 haplotype data. American Journal of Human Genetics, 73:63-73, 2003. (pdf)
  11. Zhang, K., Deng, M., Chen, T., Waterman, M., and Sun, F. A dynamic programming approach for haplotype partitioning. The Proceeding of National Academy of Sciences (PNAS), 99(11): 7335-9, May 28 2002. (pdf)

 

Protein Interactions and Gene Expression

  1. Jiang R, Tu Z, Chen T and Sun F. (2006) Network Motif Identification in Stochastic Networks. The Proceeding of National Academy of Sciences (PNAS). 2006. vol 103 no 2 page 9404-9. (pdf)
  2. Lee H, Deng M, Sun F and Chen T. (2006) Inferring domain-domain interactions from multiple biological data sources. BMC Bioinformatics. 2006 May 25;7(1):269. (pdf)
  3. Tu Z, Wang L, Arbeitman M, Chen T and Sun F. An Integrative Approach for Causal Gene Identification and Gene Regulatory Pathway Inference. Bioinformatics (ISMB 2006). Jul 15;22(14):e489-96. (pdf)
  4. Tu Z, Wang L, Xu M, Zhou J, Chen T and Sun F. Further understanding human disease genes by comparing with housekeeping genes and other genes. 2006, BMC Genomics. 2006 Feb 21;7:31. (pdf)
  5. Lee H, Tu Z, Deng M, Sun F and Chen T. (2006) Diffusion kernel based logistic regression models for protein function prediction. OMICS: Integrative Biology. 2006 Spring;10(1):40-55. (pdf)
  6. Lee H, Sun F and Chen T. (2005) Assessment of the Reliability of Protein-Protein Interactions Using Protein Localization and Gene Expression Data. BIOINFO 2005. (pdf)
  7. Deng, M., Chen, T. and Sun, F. An Integrative Analysis of Protein Function Prediction. Journal of Computational Biology, 2004;11(2-3): 463-75. (pdf)
  8. Deng, M., Tu, Z., Sun, F., and Chen, T. Mapping Gene Ontology to Proteins Based on Protein-protein Interaction Data. Bioinformatics, Apr 12;20(6):895-902, 2004. (pdf)
  9. Deng, M., Zhang, K., Mehta, S., Chen, T. and Sun, F. Prediction of protein function using protein-protein interaction data.  Journal of Computational Biology, 10(6): 947-960, 2003. (pdf)
  10. Deng, M., Sun, F. and Chen, T. Assessment of the reliability of protein-protein interactions and protein function prediction. Pacific Symposium on Biocomputing. Page 140-51, 2003. (pdf)
  11. Deng, M., Metah, S., Sun, F., and Chen, T. Inferring Domain-Domain Interactions from Protein-Protein Interactions. Genome Research 12:1540-8, 2002. (pdf)
  12. Chen, T., Filkov, V. and Skiena, S. Identifying gene regulartory networks from experimental data. Journal of Parallel Computing, 27(1-2), page 141-162, 2001. (pdf)
  13. Chen, T., He, H. and Church, G.M. Modeling Gene Expression with Differential Equations. Pacific Symposium on Biocomputing (PSB99), Page 29-40, 1999. (pdf)

 

Chemical Informatics and Drug Screening

  1. Guha R, Dutta D, Wild DJ, Chen T. Counting Clusters Using R-NN Curves. J Chem Inf Model. 2007 Jul-Aug;47(4):1308-18. Epub 2007 Jun 30.
  2. Dutta D, Guha R, Wild D and Chen T. Ensemble Feature Selection: Consistent Descriptor Subsets for Multiple QSAR Models. ACS J. Chem. Inf. Model. 2007. May-Jun;47(3):989-97.
  3. Guha R, Dutta D, Jurs P. and Chen T. R-NN Curves: An Intuitive Approach to Outlier Detection Using a Distance Based Method.  ACS J. Chem. Inf. Model. 2006.  Jul-Aug;46(4):1713-22. (pdf)     
  4. Dutta D, Guha R, Jurs P. and Chen T. Local Lazy Regression: Making Use of the Neighborhood to Improve QSAR Predictions. ACS J. Chem. Inf. Model., 2006. Jul-Aug;46(4):1836-47. (pdf)   
  5. Dutta D, Guha R, Jurs P. and Chen T. Scalable Partitioning and Exploration of Chemical Spaces Using Geometric Hashing. ACS J. Chem. Inf. Model., 2006. Jan-Feb;46(1):321-33. (pdf)   

 

Books and Book Chapters

  1. Sun FZ, Chen T, Deng MH, Lee HJ, Tu ZD (2006) Data integration for the study of protein interactions. In Rudy Guerra and David Allison: Meta-analysis and Combining Information in Genetics, Chapman and Hall.
  2. Huang, Y.-T., Zhang, K., Chen, T., and Chao, K. -M. (2006) “Approximation Algorithms for the Selection of Robust Tag SNPs,” a chapter of the book “Handbook of Approximation Algorithms and Metaheuristics," edited by Teofilo F. Gonzalez (University of California, Santa Barbara), to be published by Chapman & Hall/CRC Press, USA.
  3. BW Lu, BA Soreghan, SN Thomas, T Chen, & AJ Yang. (2005) Proteomics data management and visualization. Book chapter.
  4. Borchers, C., Chen, T. and Neamati, N. Book Chapter: Application of Proteomics in Biological Sciences. Molecular Carcinogenesis. Editor: Joseph R. Landolph's and David Warshawsky. CRC Press, 2003.
  5. Chen, T. and Waterman, M. Book Chapter: Dynamic Programming. Nature Encyclopedia of Human Genome, Nature Publishing, 2003.

 

Other Works

  1. Smith E, Meyerrose TE, Kohler T, Namdar-Attar M, Bab N, Lahat O, Noh T, Li J, Karaman MW, Hacia JG, Chen T, Nolta JA, Muller R, Bab I, Frenkel B. Leaky ribosomal scanning in mammalian genomes: Physiological consequences of histone H4 alternative translation. Nucleic Acid Research. 2005 Mar 1;33(4):1298-308. (pdf)
  2. Wyrick, J. J., Aparicio, J. G., Chen, T., Barnett, J. D., Jennings, E. G., Young, R. A., Bell, S. P., and Aparicio, O. M. Genome-Wide Location Analysis of ORC and MCM Proteins: High Resolution Mapping of Replication Origins Reveals Novel Origin Classes in Saccharomyces cerevisiae. Science, 2357-2360, Dec 14, 2001. (pdf)
  3. Chen, T. and Skiena, S. A Case Study in Genome-Level Fragment Assembly. Bioinformatics 2000 16:494-500. (pdf)
  4. Chen, T. and Kao, M.Y., On the Informational Asymmetry between Upper and Lower Bounds for Ultrametric Evolutionary Trees. The 7th Annual European Symposium on Algorithms (ESA’99), Page 248-256, 1999. LNCS, Lecture Notes in Computer Science, Springer-Verlag. (pdf)
  5. Chen, T. and Zhang, M.Q. Pombe: A Fission Yeast gene-finding and exon-intron structure prediction system. Yeast, Vol. 14: 701-710, 1998. (pdf)
  6. Chen, T. and Skiena, S. Trie-based data structures for fragment assembly. The Eighth Symposium on Combinatorial Pattern Matching (CPM97), page 206-223, 1997. (pdf)
  7. Chen, T. and Skiena, S. Sorting with fixed-length reversals. Journal of Discrete Applied Mathematics, Special Volume on Computational Molecular Biology, page 269-296, vol.71, December 5 1996. (pdf)

Software Development

·      Mass Spectrometry Software: Peptide Identification via Tandem Mass Spectrometry (http://msms.cmb.usc.edu/ ).

·      Haplotype Block Partitioning: The Dynamic Programming Algorithms for Haplotype Block Partitioning and Tag SNPs Selection by Haplotype Data and Genotype Data (http://www.cmb.usc.edu/msms/HapBlock/).

·      Protein-Protein Interaction Reliability: A Maximum Likelihood Estimation (MLE) Based-algorithm for Estimating the Reliability of Observed Protein Interactions Combining Gene Expression Profiles and Localization. (http://msms.usc.edu/~hyunjul/rel/rel.html )

·      Prediction of Domain-Interaction: An Integrated Approach for Predicting Domain Interactions (http://msms.usc.edu/~hyunjul/emddi/emddi.html )

·      Protein Function Prediction-KLR: A Kernel Logistic Regression (KLR) Based Approach for Protein Function Prediction (http://msms.usc.edu/~hyunjul/klr/klr.html )

·      Pathway Identification: An Integrative Approach for Causal Gene Inference and Pathway Identification (http://www-rcf.usc.edu/~fsun/Programs/causal_pathway/ismb2006.html )

·      Network Motif Identification: An Expectation-Maximization (EM) Algorithm for Network Motif Identification (http://www-rcf.usc.edu/~fsun/Programs/EM_NM/emnmmain.html )