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PUBLICATIONS

Peer-reviewed articles, preprints, and book chapters

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Selected publications

[* Joint primary author] [† Joint corresponding author]

Preprints

  1. Leveraging public transcriptomes to delineate sex- and age-associated gene signatures and pan-body processes
    Johnson KA, Krishnan A
    bioRxiv (2023)
     

  2. nleval: A Python toolkit for generating benchmarking datasets for machine learning with biological networks
    Liu R, Krishnan A
    bioRxiv (2023)
    [software: nleval]
     

  3. The topological shape of gene expression across the evolution of flowering plants
    Palande S, Kaste JAM, …, Krishnan A, …, Thompson AM, Rougon-Cardoso A, Chitwood DH, VanBuren R
    bioRxiv (2022)
     

  4. Co-expression signatures of combinatorial gene regulation
    Gomez-Cano F, Xu Q, Shiu SH, Krishnan A, Grotewold E
    bioRxiv (2020) 10.1101/2020.05.19.104935

Peer-Reviewed Journal Papers

  1. PyGenePlexus: A Python package for gene discovery using network-based machine learning
    Mancuso CA*, Liu R*, Krishnan A
    Bioinformatics (2023) 39:btad064.
    [pdf] [bioRxiv] [pubmed]
    [software: PyGenePlexus]
     

  2. Accurately modeling biased random walks on weighted graphs using node2vec+
    Liu R, Hirn M, Krishnan A
    Bioinformatics (2023) 39:btad047.
    [pdf] [arXiv] [pubmed]
    [software: PecanPy] [data]
     

  3. A network-based approach for isolating the chronic inflammation gene signatures underlying complex diseases towards finding new treatment opportunities
    Hickey SL*, McKim A*, Mancuso CA, Krishnan A
    Frontiers in Pharmacology (2022) 13:995459.
    [pdf] [bioRxiv] [pubmed]
    [code] [data]
     

  4. GenePlexus: A web-server for network-based machine learning for human gene classification
    Mancuso CA, Bills P, Newsted J, Krum D, Liu R, Krishnan A
    Nucleic Acids Research (2022) 50:W358.
    [pdf] [bioRxiv] [pubmed]
    [web-server: GenePlexus]
     

  5. Systematic tissue annotations of genomics samples by modeling unstructured metadata
    Hawkins NT, Maldaver M, Yannakopoulos A, Guare LA, Krishnan A
    Nature Communications (2022) 13:6736.
    [pdf] [bioRxiv] [pubmed]
    [code: Txt2Onto]
     

  6. Robust normalization and transformation techniques for constructing gene coexpression networks from RNA-seq data
    Johnson KA, Krishnan A
    Genome Biology (2022) 23:1.
    [pdf] [bioRxiv] [pubmed]
    [code] [data] [results web-interface]
     

  7. Combinatorial patterns of gene expression changes contribute to variable expressivity of the developmental delay-associated 16p12.1 deletion
    Jensen M, Tyryshkina A, Pizzo L, Smolen C, Das M, Huber E, Krishnan A, Girirajan S
    Genome Meidicine (2021) 13:163
    [pdf] [bioRxiv] [pubmed]
     

  8. Reconciling multiple connectivity scores for drug repurposing
    Samart K*, Tuyishime P*, Krishnan A†, Ravi J
    Briefings in Bioinformatics (2021) 22:bbab161.
    [pdf] [arXiv] [pubmed]
    [repo] [live document]
     

  9. PecanPy: a fast, efficient, and parallelized Python implementation of node2vec
    Liu R, Krishnan A
    Bioinformatics (2021) 37:3377.
    [pdf] [bioRxiv] [pubmed]
    [code: GitHub, PyPI]
     

  10. A flexible, interpretable, and accurate approach for imputing the expression of unmeasured genes
    Mancuso CA*, Canfield JL*, Singla D, Krishnan A
    Nucleic Acids Research (2020) 48:e125.
    [pdf] [bioRxiv] [pubmed]
    [code: Expresto] [data]
     

  11. Supervised-learning is an accurate method for network-based gene classification
    Liu R*, Mancuso CA*, Yannakopoulos A, Johnson KA, Krishnan A
    Bioinformatics (2020) 36:3457.
    [pdf] [bioRxiv] [pubmed]
    [code] [data]
     

  12. A computational framework for genome-wide characterization of the human disease landscape
    Lee Y, Krishnan A, Oughtred  R, Rust J, Chang CS, Ryu J, Kristensen VN, Dolinski K, Theesfeld CL, Troyanskaya OG
    Cell Systems (2019) 8:152.e6.
    [pdf] [pubmed]
    [web-interface: URSAHD]
     

  13. Pervasive genetic interactions modulate neurodevelopmental defects of autism-associated 16p11.2 deletion in Drosophila melanogaster
    Iyer J, Singh MD, Jensen M, Patel P, Pizzo L, Huber E, Koerselman H, Weiner AT, Lepanto P, Vadodaria K, Kubina A, Wang Q, Talbert A, Yennawar S, Badano J, Manak R, Rolls MM, Krishnan A, Girirajan S
    Nature Communications (2018) 9:2548.
    [pdf] [bioRxiv] [pubmed] [press: psu | spectrum]
     

  14. GIANT 2.0: genome-scale integrated analysis of gene networks in tissues
    Wong AK, Krishnan A, Troyanskaya OG
    Nucleic Acids Research (2018) 46:W65.
    [pdf] [pubmed]
    [web-interface: GIANT2]
     

  15. RECoN: Rice environment coexpression network for systems level analysis of abiotic-stress response
    Krishnan A, Gupta C, Ambavaram MMR, Pereira A
    Frontiers in Plant Science (2017) 8:1640.
    [pdf] [bioRxiv] [pubmed]
    [web-interface: RECoN]
     

  16. Integrative networks illuminate biological factors underlying gene-disease associations
    Krishnan A†, Taroni JN, Greene CS†
    Current Genetic Medicine Reports (2016) 4:155.
    [pdf] [bioRxiv]
     

  17. Genome-wide prediction and functional characterization of the genetic basis of autism spectrum disorder
    Krishnan A*, Zhang R*, Yao V, Theesfeld CL, Wong AK, Tadych A, Volfovsky N, Packer A, Lash A, Troyanskaya OG
    Nature Neuroscience (2016) 19:1454.
    [pdf] [bioRxiv] [pubmed] [press: princeton | simons foundation | popular science | researchgate]
    [web-interface: ASD]
     

  18. Understanding multi-cellular function and disease with human tissue-specific gene interaction networks
    Greene CS*, Krishnan A*, Wong AK*, Ricciotti E, Zelaya R, Himmelstein D, Chasman D, Fitzgerald G, Dolinski K, Grosser T, Troyanskaya OG
    Nature Genetics (2015) 47:569.
    [pdf] [pubmed] [commentary: nature biotechnology]
    [web-interface: GIANT]
     

  19. Tissue-aware data integration approach for the inference of pathway interactions in metazoan organisms
    Park C, Krishnan A, Zhu Q, Wong AK, Lee Y, Troyanskaya OG
    Bioinformatics (2015) 31:1093.
    [pubmed]
    [web-interface: PathwayNet]
     

  20. Coordinate regulation of photosynthetic carbon metabolism for yield and environmental stress response in rice
    Ambavaram MM, Basu S, Krishnan A, Venkategowda R, Batlang U, Rahman L, Baisakh N, Pereira A
    Nature Communications (2014) 5:5302.
    [pubmed]
     

  21. Ontology-aware classification of tissue and cell-type signals in gene expression profiles across platforms and technologies
    Lee Y, Krishnan A, Zhu Q, Troyanskaya O
    Bioinformatics (2013) 29:3036.
    [pubmed]
    [web-interface: URSA]
     

  22. Coordinated activation of cellulose and repression of lignin biosynthesis pathways in rice
    Ambavaram MM*, Krishnan A*, Trijatmiko KR, Pereira A
    Plant Physiology (2011) 155:916.
    [pubmed]
     

  23. Molecular and physiological analysis of drought stress in Arabidopsis reveals early responses leading to acclimation in plant growth
    Harb A, Krishnan A, Pereira A
    Plant Physiology (2010) 154:1254.
    [pubmed]
     

  24. Diversity of En/Spm transposons in maize and rice
    Krishnan A, Greco R, Pereira A
    Maydica (2009) 53:181.
     

  25. Mutant resources in rice for functional genomics of the grasses
    Krishnan A, Guiderdoni E, An G, Hsing YC, Han C, Lee MC, Yu SM, Upadhyaya N, Ramachandran S, Zhang Q, Sundaresan V, Hirochika H, Leung H, Pereira A
    Plant Physiology (2009) 149:165.
    [pubmed]
     

  26. Integrative approaches for mining transcriptional regulatory programs in Arabidopsis
    Krishnan A, Greco R, Pereira A
    Briefings in Functional Genomics and Proteomics (2008) 7:264.
    [pubmed]

Book Chapters

  1. Microarray data analysis
    Mohapatra SK*, Krishnan A*.
    Plant Reverse Genetics (2009) The Humana Press Inc., Totowa NJ, USA.
     

  2. Genetic networks underlying plant abiotic stress responses
    Krishnan A, Ambavaram MMR, Harb A, Batlang U, Wittich PE, Pereira A.
    Genes for Plant Abiotic Stress (2009) John Wiley & Sons, Inc., Ames IA, USA.

Other publications

  1. Functional assessment of the “two-hit” model for neurodevelopmental defects in Drosophila and X. laevis
    Pizzo L*, Lasser M*, ..., Krishnan A, Rolls M, Lowery LA, Girirajan S.
    Accepted in PLoS Genetics (2020) bioRxiv: doi.org/10.1101/2020.09.14.295923
     

  2. Rare variants in the genetic background modulate the expressivity of neurodevelopmental disorders
    Pizzo L, Jensen M, Polyak A, Rosenfeld JA, Mannik K, Krishnan A, …, Girirajan S
    Genetics in Medicine (2018).
    [biorxiv] [pubmed]
     

  3. A loop-counting method for covariate-corrected low-rank biclustering of gene-expression and genome-wide association study data
    Rangan AV , McGrouther CC, Kelsoe J, Schork N, Stahl E, Zhu Q, Krishnan A, Yao V, Troyanskaya OG, Bilaloglu S, Raghavan P, Bergen S, Jureus A, Landen M, Bipolar Disorders Working Group of the Psychiatric Genomics Consortium.
    PLoS Computational Biology  (2018) 14: e1006105.
    [pubmed]
     

  4. SANe: The seed active network for mining transcriptional regulatory programs of seed development
    Gupta C, Krishnan A, Collakova E, Wolinski P, Pereira A.
    bioRxiv (2017) doi:10.1101/165894
    [web-interface: SANe]
     

  5. IMP 2.0: A multi-species functional genomics portal for integration, visualization and prediction of protein functions and networks
    Wong AK, Krishnan A, Yao V, Tadych A, Troyanskaya OG.
    Nucleic Acids Research (2015) 43:W128.
    [pubmed]
     

  6. FNTM: a server for predicting Functional Networks of Tissues in Mouse
    Goya J*, Wong AK*, Yao V*, Krishnan A, Homilius M, Troyanskaya OG.
    Nucleic Acids Research (2015) 43:W182.
    [pubmed]
     

  7. Low variance RNAs identify Parkinson’s disease molecular signature in blood
    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.
    Movement Disorders (2015) 30:813.
    [pubmed]
     

  8. Targeted exploration and analysis of large cross-platform human transcriptomic compendia
    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.
    Nature Methods (2015) 12:211.
    [pubmed]
     

  9. Drought responsive genes and their functional terms identified by GS FLX Pyro sequencing in maize
    Batlang U, Ambavaram MMR, Krishnan A, Pereira A.
    Maydica 59:306.
     

  10. Rice GROWTH UNDER DROUGHT KINASE is required for drought tolerance and grain yield under normal and drought stress conditions
    Venkategowda R, Basu S, Krishnan A, Pereira A.
    Plant Physiology (2014) 166:1634.
    [pubmed]
     

  11. Reconciling differential gene expression data with molecular interaction networks
    Poirel CL, Rahman A, Rodrigues RR, Krishnan A, Addesa JR, Murali TM.
    Bioinformatics (2013) 29:622.
    [pubmed]
     

  12. Stochastic modeling of dwell-time distributions during transcriptional pausing and initiation
    Xu X, Kumar N, Krishnan A, Kulkarni R
    52nd IEEE Conference on Decision and Control (2013) 4068.
    [ieee]
     

  13. Effects of drought on gene expression in maize reproductive and leaf meristem tissue revealed by RNA-Seq
    Kakumanu A, Ambavaram MM, Klumas C, Krishnan A, Batlang U, Myers E, Grene R, Pereira A.
    Plant Physiology (2012) 160:846.
    [pubmed]
     

  14. Mechanisms of action and medicinal applications of abscisic acid
    Bassaganya-Riera J, Skoneczka J, Kingston DG, Krishnan A, Misyak S, Carter A, Pereira A, Guri AJ, Tumarkin R, Hontecillas R.
    Current Medicinal Chemistry (2009) 17:467.
    [pubmed]
     

  15. Improvement of water use efficiency in rice by expression of HARDY, an Arabidopsis drought and salt tolerance gene
    Karaba A, Dixit S, Greco R, Aharoni A, Trijatmiko KR, Marsch-Martinez N, Krishnan A, Nataraja KN, Udayakumar M, Pereira A.
    Proceedings of the National Academy of Sciences USA (2007) 104:15270.
    [pubmed]

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