Karthik Devarajan, PhD
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Fox Chase Cancer Center
333 Cottman Avenue
Philadelphia, PA 19111
- PhD, Northern Illinois University, 2000
- MSc, Tech, Birla Institute of Technology & Science, India, 1992
- Unsupervised dimension reduction
- Supervised and semi-supervised dimension reduction
- Assessment of technical reproducibility and outlier detection in large-scale biological data
- Biomarker discovery
Advances in high-throughput technologies in the past decade have given rise to large-scale biological data that is measured in a variety of scales. Gene expression studies enable the simultaneous measurement of the expression profiles of tens of thousands of genes and proteins, often from only a handful of biological samples. Data is typically presented as a two-way numeric table in which the rows represent the genes, columns represent the samples and each entry consists of the expression level of a given gene in a given sample. The samples may represent a phenotype such as tissue type, experimental condition or time points. Traditionally these studies have involved the use of microarray technology to measure mRNA expression, and more recently, the use of SNP arrays to measure allele-specific expression and DNA copy number variation, methylation arrays to quantify DNA methylation and next-generation sequencing technologies, such as RNA-Seq and ChIP-Seq, for the measurement of digital gene expression. In addition, high-throughput compound and siRNA screening assays are specifically designed to detect interactions with compounds by directly measuring inhibition of siRNA or kinase activity.
These studies have resulted in massive amounts of data requiring analysis and interpretation while offering tremendous potential for growth in our understanding of the pathophysiology of many diseases. The focus of my research is in the development of novel statistical methodology for the analysis of data stemming from such high-throughput studies. It includes methods for dimension reduction and molecular pattern discovery as well as for correlating a qualitative or quantitative outcome variable (including tissue type, presence of disease, patient response to treatment, survival time) with large numbers of covariates (genes, SNPs or sequence tags) based on supervised and unsupervised learning. The primary focus of my research activities consist of the following two problems from statistical learning theory: nonnegative matrix factorization (NMF) and continuum regression (CR).
Devarajan K, Wang G, Ebrahimi N. A unified statistical approach to non-negative matrix factorization and probabilistic latent semantic indexing. Mach Learn. 2015 Apr 1;99(1):137-163. PMID: 25821345; PMCID: PMC4371760. COBRA pre-print series, Article 80. (July 2011). http://biostats.bepress.com/cobra/art80.
Devarajan K, Cheung VC. On nonnegative matrix factorization algorithms for signal-dependent noise with application to electromyography data. Neural Comput. 2014 Jun;26(6):1128-68. doi: 10.1162/NECO_a_00576. Epub 2014 Mar 31. PMID: 24684448. PubMed
Devarajan K, Ebrahimi N. On penalized likelihood estimation for a non-proportional hazards regression model. Stat Probab Lett. 2013 Jul;83(7):1703-1710. PMID: 24791034; PMCID: PMC4001813. PubMed
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; PMCID: PMC2976538. PubMed
Devarajan K, Zhou Y, Chachra N, Ebrahimi N. A supervised approach for predicting patient survival with gene expression data. Proc IEEE Int Symp Bioinformatics Bioeng. 2010;2010(5521718):26-31. PMID: 20865131; PMCID: PMC2941901. PubMed
Devarajan K. Nonnegative matrix factorization: an analytical and interpretive tool in computational biology. PLoS Comput Biol. 2008 Jul 25;4(7):e1000029. doi: 10.1371/journal.pcbi.1000029. Review. PMID: 18654623; PMCID: PMC2447881. PubMed
Cheung, V.C.K., Devarajan, K., Severini, G., Turolla, A., Bonato, P. Decomposing time series data by a non-negative matrix factorization algorithm with temporally constrained coefficients. Proceedings (IEEE Int Conference in Engineering in Medicine and Biology) 2015.
Wang M, Block TM, Marrero J, Di Bisceglie AM, Devarajan K, Mehta A. Improved biomarker performance for the detection of hepatocellular carcinoma by inclusion of clinical parameters. Proceedings (IEEE Int Conf Bioinformatics Biomed). 2012 Dec;2012. doi: 10.1109/BIBM.2012.6392612. PMID: 24307972; PMCID: PMC3845221. PubMed
Anastassiadis T, Deacon SW, Devarajan K, Ma H, Peterson JR. Comprehensive assay of kinase catalytic activity reveals features of kinase inhibitor selectivity. Nat Biotechnol. 2011 Oct 30;29(11):1039-45. doi: 10.1038/nbt.2017. PMID: 22037377; PMCID: PMC3230241. PubMed
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. doi: 10.1016/j.cell.2011.06.020. Epub 2011 Jun 30. PMID: 21722948; PMCID: PMC3230223. PubMed
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. doi: 10.1126/scisignal.2001083. PMID: 20858866; PMCID: PMC2950064. PubMed
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.