• Associate Professor
• PhD, University of Washington, Biostatistics, 2000

Research Interests

Dr. Ghosh's research interests are primarily in statistical methods for the analysis of large-scale genetic and genomic datasets in biological experiments. Many of his substantive collaborations have been with biologists generating high-dimensional datasets using modern high-throughput molecular assays. This forces biologists to deal with making sense of these high-dimensional genomic datasets. He has experience dealing with the various steps of analysis that are needed in the analysis of functional genomic data. These include the following: preprocessing, normalization, differential expression, clustering and classification. He is particular interested in methods for integrating data across diverse genomic platforms as well as incorporating biological knowledge in the statistical analysis of high-throughput biological data in human disease settings.

Consideration of these high-dimensional datasets has also led to Dr. Ghosh's interest in more methodological problems, most recently involving multiple testing procedures. He has been involved with the development of Empirical Bayes multiple testing procedures for high-dimensional data. This has led to a methodology I term shrunken p-values for assessment of differential expression (SPADE). I am currently working on a unified testing and estimation framework for such problems.

Finally, I have general research interests in semiparametric models and survival analysis.

Representative Publications:

Ghosh, D. (2007). Incorporating monotonicity into the evaluation of a biomarker. Biostatistics 8, 402 - 413.

Poisson, L. M. and Ghosh, D. (2007). Statistical issues and analyses of in vitro and in vivo genomic data in order to identify clinically relevant profiles. Cancer Informatics 1, 231 - 243.

Ghosh, D. (2006). Shrunken p-values for assessing differential expression, with applications to genomic data analysis. Biometrics 62, 1099 - 1106.

Banerjee, M., Biswas, P. and Ghosh, D. (2006). Semiparametric binary regression under monotonicity constraints. Scandinavian Journal of Statistics 33, 673 - 697.

Chang, S., Ghosh, D., Linderman, J. L. and Kirschner, D. E. (2006). Length-based prediction of peptide-MHC class II binding affinity. Bioinformatics 22, 2761 - 2767.

Last updated: 15 August 2007