Arjun Krishnan, PhD
Visiting Associate Professor, Biomedical Informatics

Graduate School :
  • PhD, Virginia Polytechnic Institute And State University (Virginia Tech) (2010)
Department: Biomedical Informatics

Professional Titles

  • Associate Professor

Research Interests

My research group develops computational approaches that take advantage of massive public data collections to build predictive and interpretable models of genes, molecular networks, and tissue mechanisms that underlie the heterogeneity of complex diseases. In addition to biomedical data science and machine learning, I am passionate about open science, research training, and creating diverse and inclusive learning environments


  • Palande S, Kaste JAM, Roberts MD, Segura Abá K, Claucherty C, Dacon J, Doko R, Jayakody TB, Jeffery HR, Kelly N, Manousidaki A, Parks HM, Roggenkamp EM, Schumacher AM, Yang J, Percival S, Pardo J, Husbands AY, Krishnan A, Montgomery BL, Munch E, Thompson AM, Rougon-Cardoso A, Chitwood DH, VanBuren R. Topological data analysis reveals a core gene expression backbone that defines form and function across flowering plants. PLoS Biol. 2023 Dec;21(12):e3002397. PubMed PMID: 38051702
  • Mancuso CA, Liu R, Krishnan A. PyGenePlexus: a Python package for gene discovery using network-based machine learning. Bioinformatics. 2023 Feb 3;39(2). PubMed PMID: 36721325
  • Liu R, Hirn M, Krishnan A. Accurately modeling biased random walks on weighted networks using node2vec. Bioinformatics. 2023 Jan 1;39(1). PubMed PMID: 36688699
  • Liu R, Krishnan A. Open Biomedical Network Benchmark, a Python toolkit for benchmarking datasets with biomedical networks. Proceedings of Machine Learning Research 2023.
  • Mancuso CA, Johnson KA, Liu R, Krishnan A. Joint representation of gene networks from multiple species improves gene classification. bioRxiv 2023.
  • Johnson KA, Krishnan A. Leveraging public transcriptomes to delineate sex- and age-associated gene signatures and pan-body processes. bioRxiv 2023.
  • Palande S, …, Husbands AY, Krishnan A, Percival S, Munch E, VanBuren R, Chitwood DH, Rougon-Cardoso A. A data-driven evaluation of Arabidopsis-centric research and the model species concept. bioRxiv 2023.
  • Zitnik M, Li MM, Wells A, Glass K, Gysi DM, Krishnan A, Murali TM, Radivojac P, Roy S, …, Milenkovic T. Current and future directions in network biology. arXiv 2023.
  • Liu R, Yuan H, Johnson KA, Krishnan A. CONE: COntext-specific Network Embedding via Contextualized Graph Attention. bioRxiv 2023.
  • Hawkins NT, Maldaver M, Yannakopoulos A, Guare LA, Krishnan A. Systematic tissue annotations of genomics samples by modeling unstructured metadata. Nat Commun. 2022 Nov 8;13(1):6736. PubMed PMID: 36347858
  • Hickey SL, McKim A, Mancuso CA, Krishnan A. A network-based approach for isolating the chronic inflammation gene signatures underlying complex diseases towards finding new treatment opportunities. Front Pharmacol. 2022;13:995459. PubMed PMID: 36313344
  • Mancuso CA, Bills PS, Krum D, Newsted J, Liu R, Krishnan A. GenePlexus: a web-server for gene discovery using network-based machine learning. Nucleic Acids Res. 2022 May 17;50(W1):W358-66. [Epub ahead of print] PubMed PMID: 35580053
  • Johnson KA, Krishnan A. Robust normalization and transformation techniques for constructing gene coexpression networks from RNA-seq data. Genome Biol. 2022 Jan 3;23(1):1. PubMed PMID: 34980209
  • S. Palande, JAM Kaste, MD Roberts, KS Abá, C Claucherty, J Dacon, R Doko, TB Jayakody, HR Jeffery, N Kelly, A Manousidaki, HM Parks, EM Roggenkamp, AM Schumacher, J Yang, S Percival, J Pardo, AY Husbands, A Krishnan, BL Montgomery, E Munch, AM Thompson, A Rougon-Cardoso, DH Chitwood, R VanBuren. The topological shape of gene expression across the evolution of flowering plants. bioRxiv 2022.09.07.506951; doi:
  • R Liu, M Hirn, AKrishnan. Accurately modeling biased random walks on weighted networks using node2vec+. bioRxiv 2022.08.14.503926; doi:
  • CA Mancuso, R Liu, A Krishnan. PyGenePlexus: A Python package for gene discovery using network-based machine learning. bioRxiv 2022.07.02.498552; doi:
  • Mancuso CA, Bills PS, Krum D, Newsted J, Liu R, Krishnan A. GenePlexus: a web-server for gene discovery using network-based machine learning. Nucleic Acids Res. 2022 May 17. [Epub ahead of print] PubMed PMID: 35580053
  • Johnson KA, Krishnan A. Robust normalization and transformation techniques for constructing gene coexpression networks from RNA-seq data. Genome Biol. 2022 Jan 3;23(1):1. PubMed PMID: 34980209
  • Samart K, Tuyishime P, Krishnan A, Ravi J. Reconciling multiple connectivity scores for drug repurposing. Brief Bioinform. 2021 Nov 5;22(6). PubMed PMID: 34013329
  • Pizzo L, Lasser M, Yusuff T, Jensen M, Ingraham P, Huber E, Singh MD, Monahan C, Iyer J, Desai I, Karthikeyan S, Gould DJ, Yennawar S, Weiner AT, Pounraja VK, Krishnan A, Rolls MM, Lowery LA, Girirajan S. Functional assessment of the "two-hit" model for neurodevelopmental defects in Drosophila and X. laevis. PLoS Genet. 2021 Apr;17(4):e1009112. PubMed PMID: 33819264
  • Liu R, Krishnan A. PecanPy: a fast, efficient, and parallelized Python implementation of node2vec. Bioinformatics. 2021 Mar 24. [Epub ahead of print] PubMed PMID: 33760066
  • Liu R, Mancuso CA, Yannakopoulos A, Johnson KA, Krishnan A. Supervised learning is an accurate method for network-based gene classification. Bioinformatics. 2020 Jun 1;36(11):3457-3465. PubMed PMID: 32129827
  • Mancuso CA, Canfield JL, Singla D, Krishnan A. A flexible, interpretable, and accurate approach for imputing the expression of unmeasured genes. Nucleic Acids Res. 2020 Dec 2;48(21):e125. PubMed PMID: 33074331
  • Lee YS, Krishnan A, Oughtred R, Rust J, Chang CS, Ryu J, Kristensen VN, Dolinski K, Theesfeld CL, Troyanskaya OG. A Computational Framework for Genome-wide Characterization of the Human Disease Landscape. Cell Syst. 2019 Feb 27;8(2):152-162.e6. PubMed PMID: 30685436
  • Pizzo L, Jensen M, Polyak A, Rosenfeld JA, Mannik K, Krishnan A, McCready E, Pichon O, Le Caignec C, Van Dijck A, Pope K, Voorhoeve E, Yoon J, Stankiewicz P, Cheung SW, Pazuchanics D, Huber E, Kumar V, Kember RL, Mari F, Curró A, Castiglia L, Galesi O, Avola E, Mattina T, Fichera M, Mandarà L, Vincent M, Nizon M, Mercier S, Bénéteau C, Blesson S, Martin-Coignard D, Mosca-Boidron AL, Caberg JH, Bucan M, Zeesman S, Nowaczyk MJM, Lefebvre M, Faivre L, Callier P, Skinner C, Keren B, Perrine C, Prontera P, Marle N, Renieri A, Reymond A, Kooy RF, Isidor B, Schwartz C, Romano C, Sistermans E, Amor DJ, Andrieux J, Girirajan S. Rare variants in the genetic background modulate cognitive and developmental phenotypes in individuals carrying disease-associated variants. Genet Med. 2019 Apr;21(4):816-825. PubMed PMID: 30190612
  • Rangan AV, McGrouther CC, Kelsoe J, Schork N, Stahl E, Zhu Q, Krishnan A, Yao V, Troyanskaya O, Bilaloglu S, Raghavan P, Bergen S, Jureus A, Landen M. A loop-counting method for covariate-corrected low-rank biclustering of gene-expression and genome-wide association study data. PLoS Comput Biol. 2018 May;14(5):e1006105. PubMed PMID: 29758032
  • Wong AK, Krishnan A, Troyanskaya OG. GIANT 2.0: genome-scale integrated analysis of gene networks in tissues. Nucleic Acids Res. 2018 Jul 2;46(W1):W65-W70. PubMed PMID: 29800226
  • Krishnan A, Gupta C, Ambavaram MMR, Pereira A. RECoN: Rice Environment Coexpression Network for Systems Level Analysis of Abiotic-Stress Response. Front Plant Sci. 2017;8:1640. PubMed PMID: 28979289
  • Krishnan A, Zhang R, Yao V, Theesfeld CL, Wong AK, Tadych A, Volfovsky N, Packer A, Lash A, Troyanskaya OG. Genome-wide prediction and functional characterization of the genetic basis of autism spectrum disorder. Nat Neurosci. 2016 Nov;19(11):1454-1462. PubMed PMID: 27479844
  • Chikina MD, Gerald CP, Li X, Ge Y, Pincas H, Nair VD, Wong AK, Krishnan A, Troyanskaya OG, Raymond D, Saunders-Pullman R, Bressman SB, Yue Z, Sealfon SC. Low-variance RNAs identify Parkinson's disease molecular signature in blood. Mov Disord. 2015 May;30(6):813-21. PubMed PMID: 25786808
  • Zhu Q, Wong AK, Krishnan A, Aure MR, Tadych A, Zhang R, Corney DC, Greene CS, Bongo LA, Kristensen VN, Charikar M, Li K, Troyanskaya OG. Targeted exploration and analysis of large cross-platform human transcriptomic compendia. Nat Methods. 2015 Mar;12(3):211-4, 3 p following 214. PubMed PMID: 25581801
  • Greene CS, Krishnan A, Wong AK, Ricciotti E, Zelaya RA, Himmelstein DS, Zhang R, Hartmann BM, Zaslavsky E, Sealfon SC, Chasman DI, FitzGerald GA, Dolinski K, Grosser T, Troyanskaya OG. Understanding multicellular function and disease with human tissue-specific networks. Nat Genet. 2015 Jun;47(6):569-76. PubMed PMID: 25915600
  • Wong AK, Krishnan A, Yao V, Tadych A, Troyanskaya OG. IMP 2.0: a multi-species functional genomics portal for integration, visualization and prediction of protein functions and networks. Nucleic Acids Res. 2015 Jul 1;43(W1):W128-33. PubMed PMID: 25969450
  • Goya J, Wong AK, Yao V, Krishnan A, Homilius M, Troyanskaya OG. FNTM: a server for predicting functional networks of tissues in mouse. Nucleic Acids Res. 2015 Jul 1;43(W1):W182-7. PubMed PMID: 25940632
  • Park CY, Krishnan A, Zhu Q, Wong AK, Lee YS, Troyanskaya OG. Tissue-aware data integration approach for the inference of pathway interactions in metazoan organisms. Bioinformatics. 2015 Apr 1;31(7):1093-101. PubMed PMID: 25431329
  • Ramegowda V, Basu S, Krishnan A, Pereira A. Rice GROWTH UNDER DROUGHT KINASE is required for drought tolerance and grain yield under normal and drought stress conditions. Plant Physiol. 2014 Nov;166(3):1634-45. PubMed PMID: 25209982
  • Poirel CL, Rahman A, Rodrigues RR, Krishnan A, Addesa JR, Murali TM. Reconciling differential gene expression data with molecular interaction networks. Bioinformatics. 2013 Mar 1;29(5):622-9. PubMed PMID: 23314326
  • Lee YS, Krishnan A, Zhu Q, Troyanskaya OG. Ontology-aware classification of tissue and cell-type signals in gene expression profiles across platforms and technologies. Bioinformatics. 2013 Dec 1;29(23):3036-44. PubMed PMID: 24037214
  • Kakumanu A, Ambavaram MM, Klumas C, Krishnan A, Batlang U, Myers E, Grene R, Pereira A. Effects of drought on gene expression in maize reproductive and leaf meristem tissue revealed by RNA-Seq. Plant Physiol. 2012 Oct;160(2):846-67. PubMed PMID: 22837360
  • Ambavaram MM, Krishnan A, Trijatmiko KR, Pereira A. Coordinated activation of cellulose and repression of lignin biosynthesis pathways in rice. Plant Physiol. 2011 Feb;155(2):916-31. PubMed PMID: 21205614
  • Mohapatra SK, Krishnan A. Microarray data analysis. Methods Mol Biol. 2011;678:27-43. PubMed PMID: 20931370
  • Harb A, Krishnan A, Ambavaram MM, Pereira A. Molecular and physiological analysis of drought stress in Arabidopsis reveals early responses leading to acclimation in plant growth. Plant Physiol. 2010 Nov;154(3):1254-71. PubMed PMID: 20807999
  • Krishnan A, Guiderdoni E, An G, Hsing YI, Han CD, Lee MC, Yu SM, Upadhyaya N, Ramachandran S, Zhang Q, Sundaresan V, Hirochika H, Leung H, Pereira A. Mutant resources in rice for functional genomics of the grasses. Plant Physiol. 2009 Jan;149(1):165-70. PubMed PMID: 19126710
  • Krishnan A, Pereira A. Integrative approaches for mining transcriptional regulatory programs in Arabidopsis. Brief Funct Genomic Proteomic. 2008 Jul;7(4):264-74. PubMed PMID: 18632743
  • Karaba A, Dixit S, Greco R, Aharoni A, Trijatmiko KR, Marsch-Martinez N, Krishnan A, Nataraja KN, Udayakumar M, Pereira A. Improvement of water use efficiency in rice by expression of HARDY, an Arabidopsis drought and salt tolerance gene. Proc Natl Acad Sci U S A. 2007 Sep 25;104(39):15270-5. PubMed PMID: 17881564
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Practice Locations

UCHealth Anschutz Outpatient Pavilion - Anschutz Medical Campus
1635 Aurora Ct
Aurora, CO 80045

Public Speaking
Machine learning and AI in biomedicine, Big data, Data science education

General Information

Graduate Schools:
  • PhD, Virginia Polytechnic Institute And State University (Virginia Tech) (2010)
Department: Biomedical Informatics
Contact Us
CU Anschutz
Fitzsimons Building

13001 East 17th Place
Campus Box C290
Aurora, CO 80045

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