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Leping Li, Ph.D.

Biostatistics Branch

Leping Li, Ph.D.
Leping Li, Ph.D.
Principal Investigator

Tel (919) 541-5168
Fax (919) 541-4311

Curriculum Vitae (li-cv.pdf)  Download Adobe Reader
P.O. Box 12233
Mail Drop A3-03
Research Triangle Park, North Carolina 27709
Delivery Instructions

Leping Li and his staff are developing and implementing methods for detecting and discovering functional elements such as the cis-regulatory motifs in the promoter regions of genes using Markov models and Expectation Maximization (EM) methods. They are also interested in developing methods for analyzing high-dimensional data from microarray and proteomics studies. Specific areas of interests include:

  • Optimized mixed Markov models for motif identification
  • Accurate anchoring alignment of divergent sequences
  • A method for gene set enrichment analysis for continuous non-monotone relationships
  • Optimized position weight matrix (PWM) for motif detection (GAPWM)
  • A genetic algorithm/k-nearest neighbor (GA/KNN) method for microarray and proteomics data analysis

The source code and documentation for GA/KNN and GAPWM may be downloaded from the Biostatistics Branch Resources page, but more information on GA/KNN appears on Li’s Studies page.

Selected Publications

  1. Li, L., Darden, T.A., Weinberg, C.R., Levine, A.J. and Pedersen L.G. Gene assessment and sample classification for gene expression data using a genetic algorithm/k-nearest neighbor method. Combinatorial Chemistry and High Throughput Screening, 2001, 4, 727. [Abstract] ( Exit NIEHS
  2. Li, L., Weinberg, C.R., Darden, T.A. and Pedersen L.G. Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method. Bioinformatics, 2001, 17, 1131. [Download the PDF] ( Exit NIEHS
  3. Heinloth, A.N., Irwin, R.D., Boorman, G.A., Nettesheim, P., Fannin, R.D., Sieber, S.O., Snell, M.L., Tucker, C.J., Li, L., Travlos, G.S., Vansant, G., Blackshear, P.E., Tennant, R.W., Cunningham, M.L. and Paules, R.S. Gene expression profiling of rat livers reveals early indicators of potential adverse effects. Toxicol. Sci., 2004, 80, 193. [Article] ( Exit NIEHS
  4. Liu D, Umbach DM, Peddada SD, Li L, Crockett PW, Weinberg CR.  A random-periods model for expression of cell-cycle genes. Proc Natl Acad Sci USA. 2004, 101, 7240. [Abstract]( Exit NIEHS
  5. Li, L., Umbach, D.M., Terry, P. and Taylor, J.A. Application of the GA/KNN method to SELDI proteomics data. Bioinformatics, 2004, 20, 1638. [Download the PDF] ( Exit NIEHS
  6. Zhang, D. Stumpo, D.J., Graves, J.P., DeGraff, L.M., Grissom, S.F., Collins, J.B., Li, L., Zeldin, D.C. and Blackshear, P.J. Identification of potential target genes for transcription factor RFX4_v3 in the developing mouse brain. J. Neurochem., 2006, 98, 860-875. [Abstract] ( Exit NIEHS
  7. Liu, D., Peddada, S.D., Li, L. and Weinberg, C.R. Comparative analysis of activation times of circadian-related genes across tissues. BMC Bioinformatics, 2006, 7, 87. [Abstract] ( Exit NIEHS
  8. Huang, W., Umbach, D.M., and Li, L. Accurate anchoring alignment of divergent sequences. Bioinformatics, 2006, 22, 29. [Abstract] ( Exit NIEHS
  9. W. Huang, U. Ohler, D.M. Umbach, and L. Li Optimized mixed Markov models for motif identification. BMC Bioinformatics, 2006, 7, 279 [Abstract] ( Exit NIEHS
  10. L. Li, Y. Liang, and R. L. Bass. GAPWM: A genetic algorithm method for optimizing a position weight matrix. Bioinformatics, 2007, doi:10.1093/bioinformatics/btm080 [Download the PDF] ( Exit NIEHS
  11. Li, L., Bass, R.L., Liang, Y. fdrMotif: Identifying cis-elements by an EM Algorithm Coupled with False Discovery Rate Control. Bioinformatics 2008; 25, 629-636, doi: 10.1093/bioinformatics/btn009 [Download the PDF] ( Exit NIEHS
  12. Li L, Bass RL, Liang Y. fdrMotif: Identifying cis-elements by an EM algorithm coupled with false discovery rate control. Bioinformatics (Oxford, England) 2008 24(5):629-636. [Abstract] ( Exit NIEHS

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