Hongkai Ji, PhD
- Associate Professor
- Biostatistics (Primary)
615 N. Wolfe Street, Room E3638
Baltimore, Maryland 21205
PhD, Harvard University, 2007
MA, Harvard University, 2004
ME, Tsinghua University, 2002
My major research interest is to develop statistical methodology for the study of gene regulation. My current work involves developing statistical tools to detect genomic regions where protein-DNA interactions occur, to discover transcription factor binding motifs, and to identify interesting gene expression patterns. My long term goal is to establish effective and efficient statistical strategies that would allow us to decipher mammalian gene regulatory networks by synthesizing information from gene expression, protein-DNA interactions, protein-protein interactions, genome sequences, transcription factor binding motifs and existing knowledge on signaling and metabolic pathways. Knowledge of these networks will serve as the basis for us to understand human development and disease.
- Gene regulation, Genomics, ChIP-chip, Microarray, Motif, Bayesian/Empirical Bayes Methods, MCMC, Hierarchical Models
1. Vokes SA, Ji HK, McCuine S, Tenzen T, Giles S, Zhong S, Longabaugh WJ, Davidson EH, Wong WH and McMahon AP (2007) Genomic characterization of Gli-activator targets in sonic hedgehog-mediated neural patterning. Development, 134: 1977-1989.
2. Paik JH, Kollipara R, Chu G, Ji HK, Xiao YH, Ding ZH, Miao LL, Tothova Z, Horner JW, Carrasco DR, Jiang S, Gilliland DG, Chin L, Wong WH, Castrillon DH and DePinho RA (2007) FoxOs are lineage-restricted redundant tumor suppressors and critical regulators of endothelial cell homeostasis. Cell. 128 (2): 309-323.
3. Ji HK, Vokes SA and Wong WH (2006) A comparative analysis of genome-wide chromatin immunoprecipitation data for mammalian transcription factors. Nucleic Acids Res., 34: e146.
4. Ji HK and Wong WH (2006) Computational biology: toward deciphering gene regulatory information in mammalian genomes. Biometrics, 62: 645-663.
5. Kim RS, Ji HK and Wing H. Wong (2006) An improved distance measure between the expression profiles linking co-expression and co-regulation in mouse. BMC Bioinformatics, 7: 44.