| Year |
Citation |
Score |
| 2024 |
Yuan H, Mancuso CA, Johnson K, Braasch I, Krishnan A. Computational strategies for cross-species knowledge transfer and translational biomedicine. Arxiv. PMID 39184546 |
0.681 |
|
| 2024 |
Gomez-Cano F, Rodriguez J, Zhou P, Chu YH, Magnusson E, Gomez-Cano L, Krishnan A, Springer NM, de Leon N, Grotewold E. Prioritizing Metabolic Gene Regulators through Multi-Omic Network Integration in Maize. Biorxiv : the Preprint Server For Biology. PMID 38464086 DOI: 10.1101/2024.02.26.582075 |
0.422 |
|
| 2024 |
Mancuso CA, Johnson KA, Liu R, Krishnan A. Joint representation of molecular networks from multiple species improves gene classification. Plos Computational Biology. 20: e1011773. PMID 38198480 DOI: 10.1371/journal.pcbi.1011773 |
0.341 |
|
| 2023 |
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, ... ... Krishnan A, et al. Topological data analysis reveals a core gene expression backbone that defines form and function across flowering plants. Plos Biology. 21: e3002397. PMID 38051702 DOI: 10.1371/journal.pbio.3002397 |
0.431 |
|
| 2023 |
Mancuso CA, Liu R, Krishnan A. PyGenePlexus: A Python package for gene discovery using network-based machine learning. Bioinformatics (Oxford, England). PMID 36721325 DOI: 10.1093/bioinformatics/btad064 |
0.383 |
|
| 2023 |
Liu R, Hirn M, Krishnan A. Accurately modeling biased random walks on weighted networks using node2vec. Bioinformatics (Oxford, England). PMID 36688699 DOI: 10.1093/bioinformatics/btad047 |
0.303 |
|
| 2022 |
Hawkins NT, Maldaver M, Yannakopoulos A, Guare LA, Krishnan A. Systematic tissue annotations of genomics samples by modeling unstructured metadata. Nature Communications. 13: 6736. PMID 36347858 DOI: 10.1038/s41467-022-34435-x |
0.351 |
|
| 2022 |
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. Frontiers in Pharmacology. 13: 995459. PMID 36313344 DOI: 10.3389/fphar.2022.995459 |
0.415 |
|
| 2022 |
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 Research. PMID 35580053 DOI: 10.1093/nar/gkac335 |
0.426 |
|
| 2022 |
Johnson KA, Krishnan A. Robust normalization and transformation techniques for constructing gene coexpression networks from RNA-seq data. Genome Biology. 23: 1. PMID 34980209 DOI: 10.1186/s13059-021-02568-9 |
0.418 |
|
| 2021 |
Jensen M, Tyryshkina A, Pizzo L, Smolen C, Das M, Huber E, Krishnan A, Girirajan S. Combinatorial patterns of gene expression changes contribute to variable expressivity of the developmental delay-associated 16p12.1 deletion. Genome Medicine. 13: 163. PMID 34657631 DOI: 10.1186/s13073-021-00982-z |
0.293 |
|
| 2021 |
Samart K, Tuyishime P, Krishnan A, Ravi J. Reconciling multiple connectivity scores for drug repurposing. Briefings in Bioinformatics. PMID 34013329 DOI: 10.1093/bib/bbab161 |
0.633 |
|
| 2021 |
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, et al. Functional assessment of the "two-hit" model for neurodevelopmental defects in Drosophila and X. laevis. Plos Genetics. 17: e1009112. PMID 33819264 DOI: 10.1371/journal.pgen.1009112 |
0.358 |
|
| 2021 |
Liu R, Krishnan A. PecanPy: a fast, efficient, and parallelized Python implementation of node2vec. Bioinformatics (Oxford, England). PMID 33760066 DOI: 10.1093/bioinformatics/btab202 |
0.291 |
|
| 2020 |
Mancuso CA, Canfield JL, Singla D, Krishnan A. A flexible, interpretable, and accurate approach for imputing the expression of unmeasured genes. Nucleic Acids Research. PMID 33074331 DOI: 10.1093/nar/gkaa881 |
0.416 |
|
| 2020 |
Liu R, Mancuso CA, Yannakopoulos A, Johnson KA, Krishnan A. Supervised-learning is an accurate method for network-based gene classification. Bioinformatics (Oxford, England). PMID 32129827 DOI: 10.1093/Bioinformatics/Btaa150 |
0.461 |
|
| 2019 |
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 Systems. PMID 30685436 DOI: 10.1016/J.Cels.2018.12.010 |
0.654 |
|
| 2018 |
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, et al. Rare variants in the genetic background modulate cognitive and developmental phenotypes in individuals carrying disease-associated variants. Genetics in Medicine : Official Journal of the American College of Medical Genetics. PMID 30190612 DOI: 10.1038/S41436-018-0266-3 |
0.386 |
|
| 2018 |
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, ... ... Krishnan A, et al. Pervasive genetic interactions modulate neurodevelopmental defects of the autism-associated 16p11.2 deletion in Drosophila melanogaster. Nature Communications. 9: 2548. PMID 29959322 DOI: 10.1038/S41467-018-04882-6 |
0.465 |
|
| 2018 |
Wong AK, Krishnan A, Troyanskaya OG. GIANT 2.0: genome-scale integrated analysis of gene networks in tissues. Nucleic Acids Research. PMID 29800226 DOI: 10.1093/Nar/Gky408 |
0.658 |
|
| 2018 |
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 Computational Biology. 14: e1006105. PMID 29758032 DOI: 10.1371/Journal.Pcbi.1006105 |
0.727 |
|
| 2017 |
Krishnan A, Gupta C, Ambavaram MMR, Pereira A. RECoN: Rice Environment Coexpression Network for Systems Level Analysis of Abiotic-Stress Response. Frontiers in Plant Science. 8: 1640. PMID 28979289 DOI: 10.3389/Fpls.2017.01640 |
0.708 |
|
| 2016 |
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. Nature Neuroscience. PMID 27479844 DOI: 10.1038/Nn.4353 |
0.637 |
|
| 2016 |
Krishnan A, Taroni JN, Greene CS. Integrative Networks Illuminate Biological Factors Underlying Gene–Disease Associations Current Genetic Medicine Reports. 4: 155-162. DOI: 10.1007/S40142-016-0102-5 |
0.631 |
|
| 2015 |
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 Research. 43: W128-33. PMID 25969450 DOI: 10.1093/nar/gkv486 |
0.609 |
|
| 2015 |
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 Research. 43: W182-7. PMID 25940632 DOI: 10.1093/Nar/Gkv443 |
0.648 |
|
| 2015 |
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. Nature Genetics. 47: 569-76. PMID 25915600 DOI: 10.1038/Ng.3259 |
0.724 |
|
| 2015 |
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. Movement Disorders : Official Journal of the Movement Disorder Society. 30: 813-21. PMID 25786808 DOI: 10.1002/Mds.26205 |
0.699 |
|
| 2015 |
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. Nature Methods. 12: 211-4, 3 p following. PMID 25581801 DOI: 10.1038/Nmeth.3249 |
0.721 |
|
| 2015 |
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 (Oxford, England). 31: 1093-101. PMID 25431329 DOI: 10.1093/Bioinformatics/Btu786 |
0.675 |
|
| 2014 |
Ambavaram MM, Basu S, Krishnan A, Ramegowda V, Batlang U, Rahman L, Baisakh N, Pereira A. Coordinated regulation of photosynthesis in rice increases yield and tolerance to environmental stress. Nature Communications. 5: 5302. PMID 25358745 DOI: 10.1038/Ncomms6302 |
0.653 |
|
| 2014 |
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 Physiology. 166: 1634-45. PMID 25209982 DOI: 10.1104/Pp.114.248203 |
0.629 |
|
| 2014 |
Basu S, Krishnan A, Ambavaram M, Rahman L, Ramegowda V, Pereira A. Identification of genes directly regulated by a transcription factor in rice Protocol Exchange. DOI: 10.1038/Protex.2014.039 |
0.641 |
|
| 2013 |
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 (Oxford, England). 29: 3036-44. PMID 24037214 DOI: 10.1093/Bioinformatics/Btt529 |
0.689 |
|
| 2013 |
Poirel CL, Rahman A, Rodrigues RR, Krishnan A, Addesa JR, Murali TM. Reconciling differential gene expression data with molecular interaction networks. Bioinformatics (Oxford, England). 29: 622-9. PMID 23314326 DOI: 10.1093/Bioinformatics/Btt007 |
0.466 |
|
| 2012 |
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 Physiology. 160: 846-67. PMID 22837360 DOI: 10.1104/Pp.112.200444 |
0.665 |
|
| 2011 |
Ambavaram MM, Krishnan A, Trijatmiko KR, Pereira A. Coordinated activation of cellulose and repression of lignin biosynthesis pathways in rice. Plant Physiology. 155: 916-31. PMID 21205614 DOI: 10.1104/Pp.110.168641 |
0.655 |
|
| 2011 |
Mohapatra SK, Krishnan A. Microarray data analysis. Methods in Molecular Biology (Clifton, N.J.). 678: 27-43. PMID 20931370 DOI: 10.1007/978-1-60761-682-5_3 |
0.492 |
|
| 2010 |
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 Physiology. 154: 1254-71. PMID 20807999 DOI: 10.1104/Pp.110.161752 |
0.647 |
|
| 2010 |
Bassaganya-Riera J, Skoneczka J, Kingston DG, Krishnan A, Misyak SA, Guri AJ, Pereira A, Carter AB, Minorsky P, Tumarkin R, Hontecillas R. Mechanisms of action and medicinal applications of abscisic Acid. Current Medicinal Chemistry. 17: 467-78. PMID 20015036 DOI: 10.2174/092986710790226110 |
0.641 |
|
| 2009 |
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 Physiology. 149: 165-70. PMID 19126710 DOI: 10.1104/Pp.108.128918 |
0.633 |
|
| 2008 |
Krishnan A, Pereira A. Integrative approaches for mining transcriptional regulatory programs in Arabidopsis. Briefings in Functional Genomics & Proteomics. 7: 264-74. PMID 18632743 DOI: 10.1093/Bfgp/Eln035 |
0.671 |
|
| 2007 |
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. Proceedings of the National Academy of Sciences of the United States of America. 104: 15270-5. PMID 17881564 DOI: 10.1073/Pnas.0707294104 |
0.647 |
|
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