| Understanding multicellular function and disease with human tissue-specific networks CS Greene, A Krishnan, AK Wong, E Ricciotti, RA Zelaya, ... Nature genetics 47 (6), 569-576, 2015 | 925 | 2015 |
| Molecular and physiological analysis of drought stress in Arabidopsis reveals early responses leading to acclimation in plant growth A Harb, A Krishnan, MMR Ambavaram, A Pereira Plant physiology 154 (3), 1254-1271, 2010 | 862 | 2010 |
| Improvement of water use efficiency in rice by expression of HARDY, an Arabidopsis drought and salt tolerance gene A Karaba, S Dixit, R Greco, A Aharoni, KR Trijatmiko, N Marsch-Martinez, ... Proceedings of the National Academy of Sciences 104 (39), 15270-15275, 2007 | 650 | 2007 |
| Genome-wide prediction and functional characterization of the genetic basis of autism spectrum disorder A Krishnan, R Zhang, V Yao, CL Theesfeld, AK Wong, A Tadych, ... Nature neuroscience 19 (11), 1454-1462, 2016 | 458 | 2016 |
| Effects of drought on gene expression in maize reproductive and leaf meristem tissue revealed by RNA-Seq A Kakumanu, MMR Ambavaram, C Klumas, A Krishnan, U Batlang, ... Plant Physiology 160 (2), 846-867, 2012 | 384 | 2012 |
| A community computational challenge to predict the activity of pairs of compounds M Bansal, J Yang, C Karan, MP Menden, JC Costello, H Tang, G Xiao, ... Nature biotechnology 32 (12), 1213-1222, 2014 | 344 | 2014 |
| Coordinated regulation of photosynthesis in rice increases yield and tolerance to environmental stress MMR Ambavaram, S Basu, A Krishnan, V Ramegowda, U Batlang, ... Nature communications 5 (1), 5302, 2014 | 335 | 2014 |
| Mutant resources in rice for functional genomics of the grasses A Krishnan, E Guiderdoni, G An, YC Hsing, C Han, MC Lee, SM Yu, ... Plant physiology 149 (1), 165-170, 2009 | 223 | 2009 |
| Coordinated activation of cellulose and repression of lignin biosynthesis pathways in rice MMR Ambavaram, A Krishnan, KR Trijatmiko, A Pereira Plant physiology 155 (2), 916-931, 2011 | 215 | 2011 |
| Rare variants in the genetic background modulate cognitive and developmental phenotypes in individuals carrying disease-associated variants L Pizzo, M Jensen, A Polyak, JA Rosenfeld, K Mannik, A Krishnan, ... Genetics in Medicine 21 (4), 816-825, 2019 | 182 | 2019 |
| IMP 2.0: a multi-species functional genomics portal for integration, visualization and prediction of protein functions and networks AK Wong, A Krishnan, V Yao, A Tadych, OG Troyanskaya Nucleic Acids Research 43 (W1), W128-W133, 2015 | 179 | 2015 |
| Targeted exploration and analysis of large cross-platform human transcriptomic compendia Q Zhu, AK Wong, A Krishnan, MR Aure, A Tadych, R Zhang, DC Corney, ... Nature methods 12 (3), 211-214, 2015 | 153 | 2015 |
| Rice GROWTH UNDER DROUGHT KINASE is required for drought tolerance and grain yield under normal and drought stress conditions V Ramegowda, S Basu, A Krishnan, A Pereira Plant physiology 166 (3), 1634-1645, 2014 | 121 | 2014 |
| Mechanisms of action and medicinal applications of abscisic acid J Bassaganya-Riera, J Skoneczka, DGJ Kingston, A Krishnan, SA Misyak, ... Current medicinal chemistry 17 (5), 467-478, 2010 | 115 | 2010 |
| GIANT 2.0: genome-scale integrated analysis of gene networks in tissues AK Wong, A Krishnan, OG Troyanskaya Nucleic acids research 46 (W1), W65-W70, 2018 | 80 | 2018 |
| Pervasive genetic interactions modulate neurodevelopmental defects of the autism-associated 16p11.2 deletion in Drosophila melanogaster J Iyer, MD Singh, M Jensen, P Patel, L Pizzo, E Huber, H Koerselman, ... Nature communications 9 (1), 2548, 2018 | 71 | 2018 |
| Robust normalization and transformation techniques for constructing gene coexpression networks from RNA-seq data KA Johnson, A Krishnan Genome biology 23, 1-26, 2022 | 58 | 2022 |
| PecanPy: a fast, efficient and parallelized Python implementation of node2vec R Liu, A Krishnan Bioinformatics, btab202, 2021 | 58 | 2021 |
| Tissue-aware data integration approach for the inference of pathway interactions in metazoan organisms CY Park, A Krishnan, Q Zhu, AK Wong, YS Lee, OG Troyanskaya Bioinformatics 31 (7), 1093-1101, 2015 | 50 | 2015 |
| Supervised learning is an accurate method for network-based gene classification R Liu, CA Mancuso, A Yannakopoulos, KA Johnson, A Krishnan Bioinformatics 36 (11), 3457-3465, 2020 | 49 | 2020 |
Andy PereiraUniversity of Arkansas, Virginia Tech, Plant Research International-Wageningen, Max Planck Institute確認したメール アドレス: uark.edu