| Machine learning techniques in disease forecasting: a case study on rice blast prediction R Kaundal, AS Kapoor, GPS Raghava BMC Bioinformatics 7 (1), 485, 2006 | 277 | 2006 |
| Combining machine learning and homology-based approaches to accurately predict subcellular localization in Arabidopsis R Kaundal, R Saini, PX Zhao Plant Physiology 154 (1), 36-54, 2010 | 101 | 2010 |
| Genomic inventory and transcriptional analysis of Medicago truncatula transporters VA Benedito, H Li, X Dai, M Wandrey, J He, R Kaundal, I Torres-Jerez, ... Plant Physiology 152 (3), 1716-1730, 2010 | 95 | 2010 |
| Plant-mSubP: a computational framework for the prediction of single-and multi-target protein subcellular localization using integrated machine-learning approaches SS Sahu, CD Loaiza, R Kaundal AoB Plants 12 (3), plz068, 2020 | 65 | 2020 |
| Predicting genome-scale Arabidopsis-Pseudomonas syringae interactome using domain and interolog-based approaches SS Sahu, T Weirick, R Kaundal BMC bioinformatics 15, 1-8, 2014 | 53 | 2014 |
| Tris (1, 3-dichloro-2-propyl) phosphate induces genome-wide hypomethylation within early zebrafish embryos DC Volz, JK Leet, A Chen, HM Stapleton, N Katiyar, R Kaundal, Y Yu, ... Environmental science & technology 50 (18), 10255-10263, 2016 | 50 | 2016 |
| RSLpred: an integrative system for predicting subcellular localization of rice proteins combining compositional and evolutionary information R Kaundal, GPS Raghava Proteomics 9 (9), 2324-2342, 2009 | 48 | 2009 |
| PHDcleav: a SVM based method for predicting human Dicer cleavage sites using sequence and secondary structure of miRNA precursors F Ahmed, R Kaundal, GPS Raghava BMC Bioinformatics 14 (S14), S9, 2013 | 47 | 2013 |
| Molecular characterization and expression analysis of the Na+/H+ exchanger gene family in Medicago truncatula D Sandhu, MV Pudussery, R Kaundal, DL Suarez, A Kaundal, RS Sekhon Functional & integrative genomics 18, 141-153, 2018 | 29 | 2018 |
| In silico prediction of host–pathogen protein interactions in melioidosis pathogen Burkholderia pseudomallei and human reveals novel virulence factors and their … CD Loaiza, N Duhan, M Lister, R Kaundal Briefings in Bioinformatics 22 (3), bbz162, 2021 | 21 | 2021 |
| LacSubPred: predicting subtypes of Laccases, an important lignin metabolism-related enzyme class, using in silico approaches T Weirick, SS Sahu, R Mahalingam, R Kaundal BMC bioinformatics 15, 1-19, 2014 | 19 | 2014 |
| Genotype x environment interaction and stability analysis for yield and other quantitative traits in maize (Zea mays L.) under rainfed and high rainfall valley areas of the … R Kaundal, BK Sharma Research on Crops 7 (1), 171-180, 2006 | 19 | 2006 |
| Transcriptional profiling of two contrasting genotypes uncovers molecular mechanisms underlying salt tolerance in alfalfa R Kaundal, N Duhan, BR Acharya, MV Pudussery, JFS Ferreira, ... Scientific Reports 11 (1), 5210, 2021 | 18 | 2021 |
| Identification and characterization of plastid-type proteins from sequence-attributed features using machine learning R Kaundal, SS Sahu, R Verma, T Weirick BMC Bioinformatics 14 (S14), S7, 2013 | 17 | 2013 |
| Transcriptome and physio-biochemical profiling reveals differential responses of rice cultivars at reproductive-stage drought stress S Kaur, K Seem, N Duhan, S Kumar, R Kaundal, T Mohapatra International Journal of Molecular Sciences 24 (2), 1002, 2023 | 13 | 2023 |
| citSATdb: Genome-Wide Simple Sequence Repeat (SSR) Marker Database of Citrus Species for Germplasm Characterization and Crop Improvement N Duhan, M Meshram, CD Loaiza, R Kaundal Genes 11 (12), 1486, 2020 | 13 | 2020 |
| Heat and drought induced transcriptomic changes in barley varieties with contrasting stress response phenotypes R Mahalingam, N Duhan, R Kaundal, A Smertenko, T Nazarov, ... Frontiers in Plant Science 13, 1066421, 2022 | 11 | 2022 |
| PredHPI: an integrated web server platform for the detection and visualization of host–pathogen interactions using sequence-based methods CD Loaiza, R Kaundal Bioinformatics 37 (5), 622-624, 2021 | 11 | 2021 |
| deepHPI: a comprehensive deep learning platform for accurate prediction and visualization of host–pathogen protein–protein interactions R Kaundal, CD Loaiza, N Duhan, N Flann Briefings in Bioinformatics 23 (3), bbac125, 2022 | 10 | 2022 |
| Deciphering the host–pathogen interactome of the wheat–common bunt system: a step towards enhanced resilience in next generation wheat R Kataria, R Kaundal International Journal of Molecular Sciences 23 (5), 2589, 2022 | 9 | 2022 |
Gajendra PS RaghavaIndraprastha Institute of Information Technology, IIIT-Delhi , IndiaZweryfikowany adres z iiitd.ac.in
