Selected publications
[* Joint primary author] [† Joint corresponding author]
Preprints
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Leveraging public transcriptomes to delineate sex- and age-associated gene signatures and pan-body processes
Johnson KA, Krishnan A
bioRxiv (2023)
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nleval: A Python toolkit for generating benchmarking datasets for machine learning with biological networks
Liu R, Krishnan A
bioRxiv (2023)
[software: nleval]
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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)
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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
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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]
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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]
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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]
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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]
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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]
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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]
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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]
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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]
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PecanPy: a fast, efficient, and parallelized Python implementation of node2vec
Liu R, Krishnan A
Bioinformatics (2021) 37:3377.
[pdf] [bioRxiv] [pubmed]
[code: GitHub, PyPI]
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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]
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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]
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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]
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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]
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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]
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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]
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Integrative networks illuminate biological factors underlying gene-disease associations
Krishnan A†, Taroni JN, Greene CS†
Current Genetic Medicine Reports (2016) 4:155.
[pdf] [bioRxiv]
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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]
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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]
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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]
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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]
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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]
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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]
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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]
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Diversity of En/Spm transposons in maize and rice
Krishnan A, Greco R, Pereira A
Maydica (2009) 53:181.
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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]
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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
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Microarray data analysis
Mohapatra SK*, Krishnan A*.
Plant Reverse Genetics (2009) The Humana Press Inc., Totowa NJ, USA.
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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
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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
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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]
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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]
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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]
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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]
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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]
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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]
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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]
-
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.
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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]
-
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]
-
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]
-
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]
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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]
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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]