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Karthik Devarajan, PhD

Karthik Devarajan, PhD

Clinical Locations

Primary Location

Fox Chase Cancer Center
333 Cottman Avenue
Philadelphia, PA 19111

About

Assistant Professor

 

    Education, Training & Credentials

    Educational Background

    • PhD, Northern Illinois University, 2000
    • MSc, Tech, Birla Institute of Technology & Science, India, 1992

     

    Industry Experience
    • Statistical Scientist, Cancer Bioinformatics, AstraZeneca R&D Boston, Waltham, MA, 2002-2005
    • Biostatistician, Bristol-Myers Squibb Pharmaceutical Research Institute, Bristol-Myers Squibb, Princeton, NJ, 1999-2002
    Research Profile

    Research Facility

    Research Interests

    • Unsupervised dimension reduction
      We have developed unsupervised dimension reduction methods for model-based clustering of gene expression data and for text mining applications in biomedical informatics using NMF. An important, but often ignored, aspect of high-throughput genomic data is its heteroscedasticity, or signal-dependent nature of noise in the measurements. We have developed information-theoretic methods that extract relevant components from large-scale biological data by accounting for signal-dependent noise. In addition, we have developed computational tools for dimension reduction and visualization using NMF that are freely available to the academic research community. These include hpcNMF, a C++ package that uses high-performance computing clusters (www.devarajan.fccc.edu) and the R package GNMF (http://cran.r-project.org/web/packages/gnmf/index.html). Furthermore, by extending nonnegative matrix factorizations using the theory of generalized linear models, we are developing methods that provide a unified framework for the modeling and analysis of data obtained in different scales (Devarajan et al., 2015, 2015; Devarajan & Cheung, 2014, 2016; Cheung et al., 2015; Devarajan, 2008; Devarajan & Ebrahimi, 2008).
    ... Expand

    Lab Overview

    Dr. Karthik Devarajan's Lab

    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).

    People

    Prince K. Afriyie, MS

    PhD Candidate

    Lauren N Spirko, MS

    PhD Candidate

    Publications

    Selected Publications

    Devarajan K, Cheung VC. A Quasi-Likelihood Approach to Nonnegative Matrix Factorization. Neural Comput. 2016 Aug;28(8):1663-93. doi: 10.1162/NECO_a_00853. Epub 2016 Jun 27. PubMed 

    Duong-Ly KC, Devarajan K, Liang S, Horiuchi KY, Wang Y, Ma H, Peterson JR. Kinase Inhibitor Profiling Reveals Unexpected Opportunities to Inhibit Disease-Associated Mutant Kinases. Cell Rep. 2016 Feb 2;14(4):772-81. doi:10.1016/j.celrep.2015.12.080. Epub 2016 Jan 14. PubMed 

    Wang M, Devarajan K, Singal AG, Marrero JA, Dai J, Feng Z, Rinaudo JA, Srivastava S, Evans A, Hann HW, Lai Y, Yang H, Block TM, Mehta A. The Doylestown Algorithm: A Test to Improve the Performance of AFP in the Detection of Hepatocellular Carcinoma. Cancer Prev Res (Phila). 2016 Feb;9(2):172-9. doi:10.1158/1940-6207.CAPR-15-0186. Epub 2015 Dec 28. PubMed 

    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. PubMed. 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. 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. 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. 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. 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. 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. PubMed

    Bellacosa A, Godwin AK, Peri S, Devarajan K, Caretti E, Vanderveer L, Bove B, Slater C, Zhou Y, Daly M, Howard S, Campbell KS, Nicolas E, Yeung AT, Clapper ML, Crowell JA, Lynch HT, Ross E, Kopelovich L, Knudson AG. Altered gene expression in morphologically normal epithelial cells from heterozygous carriers of BRCA1 or BRCA2 mutations. Cancer Prev Res (Phila). 2010 Jan;3(1):48-61. doi: 10.1158/1940-6207.CAPR-09-0078. 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. PubMed

    Additional Publications

    My NCBI