Krishna Karuturi


  • Indian Institute of Science (Bangalore, India), 2001, PhD (Computer Science)
  • Indian Institute of Science (Bangalore, India), 1995, ME (Systems Science)
  • Osmania University (Hyderabad, India), 1993, BE (Electronics & Comm Eng)

Research Interests

1. Statistical Bioinformatics: Systems level data from the hi-throughput technologies contains multitude of information relevant to elicit the underlying biology. However, it requires development and application of suitable statistical techniques and algorithms. Hence we develop statistical technniques and computational algorithms for the orthogonal analyses of complete spectrum of omics data: (a) statistical significance assessment; (b) module discovery and network Analysis; (c) machine learning in biomedicine; (d) single cell data analysis; (e) bulk omic data analysis.

2. Predictive Genomic Medicine: Predictive genomic medicine has emerged as a major step forward in improving human health. It requires identifying driver genes and mutations, biomarkers and designing diagnostics. We collaborate and develop statistical techniques to achieve this goal especially in understanding cancer: (a) TRIAGE platform to identify driver oncogenes and RTH network analysis to identify tumor suppressor genes; (b) gene interaction based design of diagnostic classifiers; and, (c) interactome and differential interactome network analysis to elicit disease specific markers and the associated mechanisms. Many of the techniques we develop can be generalized to other diseases as well.

3. Infrastructure Analytics for Predictive Genomic Medicine: The databases of large body of biological knowledge and systems level data, standardized analytical pipelines and visualization play important role in predictive genomic medicine. We aim to develop infrastructure to facilitate data integration, flexible analytical pipeline and visualization.

Selected Publications

Complete List of Publications are available at

  • Yue Zhao, Ziwei Pan, Sandeep Namburi, Andrew Pattison, Atara Posner, Shiva Balachander, Carolyn A. Paisie, Honey V Reddi, Jens Rueter, Anthony J Gill, Stephen Fox, Kanwal P.S. Raghav, William F Flynn, Richard W. Tothill, Sheng Li, R. Krishna Murthy Karuturi, Joshy George. (2020). CUP-AI-Dx: A tool for inferring cancer tissue of origin and molecular subtype using RNA gene-expression data and artificial intelligence. EBioMedicine, 61.
  • Kumuthini J, Chimenti M, Nahnsen S, Peltzer A, Meraba R, McFadyen R, et al. (2020) Ten simple rules for providing effective bioinformatics research support. PLoS Comput Biol 16(3): e1007531.
  • Srivastava, A., George, J., & Karuturi, R. K. (2019).  Transcriptome Analysis, Encyclopedia Of Bioinformatics and Computational Biology, eds. Shoba R, Elsevier.
  • Kesarwani, A. K., Malhotra, A., Srivastava, A., Ananda, G., Ashoor, H., Kumar, P., … & Karuturi, R. K. M. (2019). Genome Informatics., Encyclopedia Of Bioinformatics and Computational Biology, eds. Shoba R, Elsevier.
  • Juntao Li, Kwok Pui Choi, Yudi Pawitan and R. Krishna Murthy Karuturi, Statistical Significance Assessment for Biological Feature Selection: Methods and Issues, Handbook of Biological Knowledge Discovery, eds. Elloumi and Zomaya, Wiley Publishers, 2013.
  • R. Krishna Murthy Karuturi, Heterogeneity of Differential Expression in Cancer Studies: Algorithms and Methods, Algorithms in Computational Biology, eds. Elloumi and Zomaya, Wiley Publishers, 2011.
  • Brian A. Joughin, Edwin C. Cheung, R. Krishna Murthy Karuturi, Julio Saez-Rodriguez, Douglas A. Lauffenburger, Edison T. Liu, Cellular Regulatory Networks, Systems Biomedicine: Concepts and Perspectives, eds. Edison T. Liu and Douglas A. Lauffenburger, Academic Press, 2009.
  • Xing Yi Woo, Anuj Srivastava, Joel Graber, Vinod Yadav, Vishal Kumar Sarsani, Al Simons, Glen Beane, Stephen Grubb, Guruprasad Ananda, Roger Liu, Grace Stafford, Jeffrey Chuang, Susan D. Airhart, , R. Krishna Murthy Karuturi, Joshy George and Carol Bult. Bioinformatics workflows for genomic analysis of tumors from Patient Derived Xenografts (PDX): challenges and guidelines, bioRxiv, September 2018.
  • William F Flynn, Sandeep Numburi, Carolyn Paise, Carol Bult, Honey Reddi, Sheng Li, R. Krishna Murthy Karuturi and Joshy George, Pan-cancer machine learning predictors of tissue of origin and molecular subtype, bioRxiv, May 2018.
  • Douglas Abraham, Parveen Kumar, R Krishna Murthy Karuturi and Joshy George, A computational method to aid the design and analysis of single cell RNA-seq experiments for cell type identification, bioRxiv, January 2018.
  • Xulong Wang, Vivek M. Philip, Guruprasad Ananda, Charles C. White, Ankit Malhotra, Paul J. Michalski, Krishna R. Murthy Karuturi, Sumana R. Chintalapudi, Casey Acklin, Michael Sasner, David A. Bennett, Philip L. De Jager, Gareth R. Howell, Gregory W. Carter, A Bayesian framework for generalized linear mixed modeling identifies new candidate loci for late-onset Alzheimer’s disease, Genetics, 208(3), March 2018.
  • Herty Liany, Jagath C Rajapakse and R Krishna Murthy Karuturi, MultiDCoX: Multi-factor Analysis of Differential Co-expression, BMC Bioinformatics, 18(S16):576, December, 2017.
  • Nathan Lawler, Alec Fabbri, Peiyong Guan, Joshy George and R. Krishna Murthy Karuturi, multiClust: An R-package for identifying biologically relevant clusters in cancer transcriptome profiles, Cancer Informatics,15:103-114, Jun 2016
  • Guruprasad Ananda, Susan Mockus, Micaela Lundquist, Vanessa Spotlow, Al Simons, Talia Mitchell, Grace Stafford, Vivek Philip, Timothy Stearns, Anuj Srivastava, Mary Barter, Lucy Rowe, Joan Malcolm, Carol Bult, Radha Krishna Murthy Karuturi, Karen Rasmussen and Douglas Hinerfeld, Development and validation of the JAX Cancer Treatment ProfileTM for detection of clinically actionable mutations in solid tumors, Experimental and Molecular Pathology, 2015.
  • Koichiro Inaki, Francesca Menghi, Xing Yi Woo, Joel P. Wagner, Pierre-Étienne Jacques, Yi Fang Lee, Phung Trang Shreckengast, Wendy WeiJia Soon, Ankit Malhotra, Audrey S.M. Teo, Axel M. Hillmer, Alexis Jiaying Khng, Xiaoan Ruan, Swee Hoe Ong, Denis Bertrand, Niranjan Nagarajan, R. Krishna Murthy Karuturi, Alfredo Hidalgo Miranda, and Edison T. Liu, Systems consequences of amplicon formation in human breast cancer, Genome Res. October 2014 24: 1559-1571
  • Sigrid Rouam, Lance D Miller and R Krishna Murthy Karuturi, Identifying Driver Genes in Cancer by Triangulating Gene Expression, Gene Location, and Survival Data, Cancer Informatics, 2014.
  • Hui CKB and R Krishna Murthy Karuturi, Differential co-expression framework to quantify goodness of biclusters and compare biclustering algorithms, Algorithms Mol Biol, 5(1):23, 2010.
  • Luo H, Li J, Eshaghi M, Liu J and Karuturi RKM, Genome-wide estimation of firing efficiencies of origins of DNA replication from time-course copy number variation data, BMC Bioinformatics, 11(1):247, 2010.
  • Yudi Pawitan, Karuturi R. Krishna Murthy and Alexander Ploner, Bias In the Estimation of False Discovery Rate and Sensitivity of Microarray Studies, Bioinformatics, 21(20):3865-3872, 2005.
  • Edison Tak-Bun Liu and Radha Krishna Murthy Karuturi, Microarrays and Clinical Investigations, The New England Journal of Medicine, 350, 1595-1597, 2004.
  • Keerthi S.S, Shevede S.K, Bhattacharyya C and K.R.K. Murthy, Improvements to Platt’s SMO Algorithm for SVM classifier Design, Neural Computation, 13(3):637-649, March’2001.
  • Shevede, S.K., Keerthi, S.S., Bhattacharyya, C. and Murthy, K.R.K., Improvements to Platt’s SMO Algorithm for Regression, IEEE Trans on Neural Networks, pp.1188-1193, Sep’2000.