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Recent advances in high-throughput technologies have given rise to large-scale biological data in the form of expression profiles of tens of thousands of genes and proteins, often with only a handful of patient samples. The focus of my research is in the development of novel statistical methodology for the analysis of large-scale data stemming from high-throughput studies such as next-generation sequencing, microarrays, allele-specific expression, SNP arrays, comparative genomics hybridization, siRNA screening and microscopy. It includes methods for dimension reduction and pattern recognition as well as for correlating a certain phenotype (such as tissue type, patient response to a certain treatment, survival time etc.) with large numbers of covariates (genes, SNPs or sequence tags).

We are currently investigating two methods from statistical learning theory - nonnegative matrix factorization and continuum regression. Specifically, we are developing unsupervised learning methods for text mining applications in biomedical informatics as well as for model-based clustering of next-generation sequencing data. In this setting, there is no prior knowledge of the expected gene expression patterns for a given set of genes or for any phenotype. Our methods are based on a unified theoretical framework for nonnegative matrix factorization for the discrimination of competing models and elucidation of clusters and hidden variables within such large-scale data. Another problem of interest is in associating large scale molecular data and clinical data with patient survival time in the presence of censoring. This is an important issue in translational medicine, however little research has been done in this area. We address this problem by developing methods that utilize continuum regression, a framework for supervised dimension reduction, in conjunction with well-known models for censored survival data.  

1. Large-scale kinase inhibitor profiling reveals features of target selectivity, Anastassiadis, T., Deacon, S., Devarajan, K., Ma, H., Peterson, J. (2011). Nature Biotechnology, In Press (to appear in Nov. issue). NIHMS328213.
2. A unified approach to non-negative matrix factorization and probabilistic latent semantic indexing" (July 2011). Devarajan, K., Wang, G., Ebrahimi, N. COBRA Preprint Series. Article 80. website
3. Cortellino S, Xu J, Sannai M, Moore R, Caretti E, Cigliano A, Le Coz M, Devarajan K, Wessels A, Soprano D, Abramowitz LK, Bartolomei MS, Rambow F, Bassi MR, Bruno T, Fanciulli M, Renner C, Klein-Szanto AJ, Matsumoto Y, Kobi D, Davidson I, Alberti C, Larue L, Bellacosa A. Thymine DNA glycosylase is essential for active DNA demethylation by linked deamination-base excision repair. Cell. 2011 Jul 8;146(1):67-79. Epub 2011 Jun 30. PMID: 21722948. PubMed
4. Frank AK, Leu JI, Zhou Y, Devarajan K, Nedelko T, Klein-Szanto A, Hollstein M, Murphy ME. The codon 72 polymorphism of p53 regulates interaction with NF-{kappa}B and transactivation of genes involved in immunity and inflammation. Mol Cell Biol. 2011 Mar;31(6):1201-13. Epub 2011 Jan 18. PMID: 21245379. PubMed
5. Devarajan K, Ebrahimi N. A semi-parametric generalization of the Cox proportional hazards regression model: Inference and Applications. Comput Stat Data Anal. 2011 Jan 1;55(1):667-676. PMID: 21076652. PubMed
6. Astsaturov I, Ratushny V, Sukhanova A, Einarson MB, Bagnyukova T, Zhou Y, Devarajan K, Silverman JS, Tikhmyanova N, Skobeleva N, Pecherskaya A, Nasto RE, Sharma C, Jablonski SA, Serebriiskii IG, Weiner LM, Golemis EA. Synthetic lethal screen of an EGFR-centered network to improve targeted therapies. Sci Signal. 2010 Sep 21;3(140):ra67. PMID: 20858866. PubMed
7. A supervised approach for predicting patient survival with gene expression data, Proceedings of the IEEE Tenth International Conference in Bioinformatics and Bioengineering, 26-31, Philadelphia, Pennsylvania, June 2010.
8. Devarajan K, Ebrahimi N. Testing for Covariate Effect in the Cox Proportional Hazards Regression Model. Commun Stat Theory Methods. 2009 Jan 1;38(14):2333-2347. PMID: 20054448. PubMed
9. Devarajan K. Nonnegative matrix factorization: an analytical and interpretive tool in computational biology. PLoS Comput Biol. 2008 Jul 25;4(7):e1000029. Review. PMID: 18654623 PubMed
10. Rennefahrt UE, Deacon SW, Parker SA, Devarajan K, Beeser A, Chernoff J, Knapp S, Turk BE, Peterson JR. Specificity profiling of Pak kinases allows identification of novel phosphorylation sites. J Biol Chem. 2007 May 25;282(21):15667-78. Epub 2007 Mar 28. PMID: 17392278. PubMed
11. Altomare DA, Vaslet CA, Skele KL, De Rienzo A, Devarajan K, Jhanwar SC, McClatchey AI, Kane AB, Testa JR. A mouse model recapitulating molecular features of human mesothelioma. Cancer Res. 2005 Sep 15;65(18):8090-5. PMID: 16166281. PubMed