Arjun Krishnan

Arjun Krishnan

Assistant Professor, Department of Computational Mathematics, Science and Engineering;
Department of Biochemistry and Molecular Biology

Room 2507H, Engineering Building
  428 S. Shaw Ln.
 (517) 432-0372
  arjun@msu.edu

https://www.arjun-krishnan.net/

About Me

B. Tech. Industrial Biotechnology, 2006, Anna University (India)

Ph.D. Genetics Bioinformatics, and Computational Biology, 2010, Virginia Tech (USA)

Arjun Krishnan received his Ph.D. in 2010 from Virginia Tech, and continued briefly as a postdoctoral researcher. There, working with Prof. Andy Pereira, he developed computational genomic methods to reconstruct the gene-regulatory programs in both model and crop plants. In 2011, he began his postdoctoral research in the Lewis-Sigler Institute for Integrative Genomics at Princeton University with Prof. Olga Troyanskaya. There, he developed integrative data-driven approaches to study tissue-specificity in the function of human genes and their association with complex diseases.

He joined the faculty of Michigan State University in January 2017 and works on developing computational approaches to study the genetic basis of biomedical phenomena relevant to human health and disease. He is primarily interested in bridging the gap between large-scale genomic/clinical data and actionable biological insights using statistical and machine learning approaches.

Research Interests

•    Genomics & Computational Biology.

•    Applied Statistical/Machine Learning.

•    Integrative Analysis of Large-scale Biological Data.

•    Genome-wide Molecular Network Models.

•    Age-specificity and Sexual-dimorphism in Health & Disease.

•    Cross-species Models for Human Disease.

•    Genetic Stratification and Precision Medicine.

Krishnan Lab for Genomics and Computational Biology
Krishnan Lab for Genomics and Computational Biology

Scientific Leader: Dr. Arjun Krishnan

The Krishnan lab develops genomics/computational approaches to gain more nuanced and accurate insights into how our genome relates to health and disease. Combining statistics/machine-learning with large-scale genomic/clinical data, we build models and predictions about the genetic basis of biomedical phenomena, especially in an age-, sex-, and tissue-specific manner. Our overarching goal is to use these methods and insights to transform our ability to link an individual's genomic profiles to her/his physiological traits, disease risks, and clinical outcomes.

Publications
[1]
Krishnan A†, Taroni JN, Greene CS†. (2016) Integrative networks illuminate biological factors underlying gene-disease associations. In Press at Current Genetic Medicine Reports doi:10.1101/062695. [† Co-corresponding author.]
 
[2]
Krishnan A*, Zhang R*, Yao V, Theesfeld CL, Wong AK, Tadych A, Volfovsky N, Packer A, Lash A, Troyanskaya OG. (2016) Genome-wide prediction and functional characterization of the genetic basis of autism spectrum disorder. Nature Neuroscience doi:10.1038/nn.4353. [* Co-first authors listed alphabetically.] [Web-interface: asd.princeton.edu]
 
[3]
Greene C*, Krishnan A*, Wong AK*, Ricciotti E, Zelaya R, Himmelstein D, Zhang R, Hartmann BM, Zaslavsky E, Sealfon SC, Chasman D, FitzGerald G, Dolinski K, Grosser T, Troyanskaya OG. (2015) Understanding multi-cellular function and disease with human tissue-specific gene interaction networks. Nature Genetics, 47:569-576. [* Co-first authors listed alphabetically.] [Web-interface: giant.princeton.edu]
 
[4]
Ambavaram MM, Basu S, Krishnan A, Venkategowda R, Batlang U, Rahman L, Baisakh N, Pereira A (2014). Coordinate regulation of photosynthetic carbon metabolism for yield and environmental stress response in rice. Nature Communications, 5:5302.

... 

Teaching

SS-18: CMSE 491 Section 003 Bioinformatics & Computational Biology

Click "Teaching" link to see past courses.