Machine learning approaches distinguish multiple stress conditions using stress-responsive genes and identify candidate genes for broad resistance in rice R Shaik, W Ramakrishna Plant physiology 164 (1), 481-495, 2014 | 143 | 2014 |
Genes and co-expression modules common to drought and bacterial stress responses in Arabidopsis and rice R Shaik, W Ramakrishna PloS one 8 (10), e77261, 2013 | 131 | 2013 |
Integrated metabolomic and proteomic approaches dissect the effect of metal-resistant bacteria on maize biomass and copper uptake K Li, VR Pidatala, R Shaik, R Datta, W Ramakrishna Environmental science & technology 48 (2), 1184-1193, 2014 | 74 | 2014 |
Bioinformatic analysis of epigenetic and microRNA mediated regulation of drought responsive genes in rice R Shaik, W Ramakrishna PloS one 7 (11), e49331, 2012 | 53 | 2012 |
Differential regulation of genes by retrotransposons in rice promoters SR Dhadi, Z Xu, R Shaik, K Driscoll, W Ramakrishna Plant Molecular Biology 87, 603-613, 2015 | 9 | 2015 |
Polymorphisms and evolutionary history of retrotransposon insertions in rice promoters Z Xu, S Rafi, W Ramakrishna Genome 54 (8), 629-638, 2011 | 8 | 2011 |
Comparative Genomics W Ramakrishna, R Shaik Genetics, Genomics and Breeding of Maize, 120, 2014 | | 2014 |
Meta Analysis of Microarray Studies Identifies Distinct Molecular Profiles of Abiotic and Biotic Stress Responses in Plants R Shaik Plant and Animal Genome XXI Conference, 2013 | | 2013 |
Dissection of stress response networks regulating multiple stresses in rice R Shaik Michigan Technological University, 2013 | | 2013 |
Classification method for microarray probe selection using sequence, thermodynamics and secondary structure parameters L Gupta, S Kumar, R Singh, R Shaik, N Dimitrova, A Gorthi, B Lakshmi, ... 2008 19th International Conference on Pattern Recognition, 1-5, 2008 | | 2008 |