| In silico approach for predicting toxicity of peptides and proteins S Gupta*, P Kapoor*, K Chaudhary*, A Gautam*, R Kumar, GPS Raghava, ... PloS one 8 (9), e73957, 2013 | 1462 | 2013 |
| Deep learning–based multi-omics integration robustly predicts survival in liver cancer K Chaudhary, OB Poirion, L Lu, LX Garmire Clinical Cancer Research 24 (6), 1248-1259, 2018 | 897 | 2018 |
| Mapping the human genetic architecture of COVID-19 CHG Initiative Nature, 2021 | 785* | 2021 |
| AKI in Hospitalized Patients with COVID-19 L Chan*, K Chaudhary*, A Saha, K Chauhan, A Vaid, S Zhao, I Paranjpe, ... Journal of the American Society of Nephrology 32 (1), 151-160, 2020 | 774 | 2020 |
| More is better: recent progress in multi-omics data integration methods S Huang, K Chaudhary, LX Garmire Frontiers in genetics 8, 84, 2017 | 686 | 2017 |
| CPPsite 2.0: a repository of experimentally validated cell-penetrating peptides P Agrawal, S Bhalla, SS Usmani, S Singh, K Chaudhary, GPS Raghava, ... Nucleic acids research 44 (D1), D1098-D1103, 2016 | 307 | 2016 |
| In Silico Models for Designing and Discovering Novel Anticancer Peptides A Tyagi, P Kapoor, R Kumar, K Chaudhary, A Gautam, GPS Raghava Scientific reports 3 (1), 2984, 2013 | 291 | 2013 |
| In silico approaches for designing highly effective cell penetrating peptides A Gautam*, K Chaudhary*, R Kumar*, A Sharma*, P Kapoor, A Tyagi, ... Journal of Translational Medicine 11 (1), 1-12, 2013 | 284 | 2013 |
| CPPsite: a curated database of cell penetrating peptides A Gautam, H Singh, A Tyagi, K Chaudhary, R Kumar, P Kapoor, ... Database 2012, bas015, 2012 | 243 | 2012 |
| Deep learning accurately predicts estrogen receptor status in breast cancer metabolomics data FM Alakwaa, K Chaudhary, LX Garmire Journal of proteome research 17 (1), 337-347, 2018 | 235 | 2018 |
| PEPstrMOD: structure prediction of peptides containing natural, non-natural and modified residues S Singh, H Singh, A Tuknait, K Chaudhary, B Singh, S Kumaran, ... Biology direct 10, 1-19, 2015 | 226 | 2015 |
| A web server and mobile app for computing hemolytic potency of peptides K Chaudhary, R Kumar, S Singh, A Tuknait, A Gautam, D Mathur, ... Scientific reports 6 (1), 22843, 2016 | 194 | 2016 |
| SATPdb: a database of structurally annotated therapeutic peptides S Singh, K Chaudhary, SK Dhanda, S Bhalla, SS Usmani, A Gautam, ... Nucleic acids research 44 (D1), D1119-D1126, 2016 | 193 | 2016 |
| AHTPDB: a comprehensive platform for analysis and presentation of antihypertensive peptides R Kumar, K Chaudhary, M Sharma, G Nagpal, JS Chauhan, S Singh, ... Nucleic acids research 43 (D1), D956-D962, 2015 | 169 | 2015 |
| Large-scale genome-wide association study of coronary artery disease in genetically diverse populations C Tcheandjieu, X Zhu, AT Hilliard, SL Clarke, V Napolioni, S Ma, KM Lee, ... Nature medicine 28 (8), 1679-1692, 2022 | 165 | 2022 |
| Peptide toxicity prediction S Gupta, P Kapoor, K Chaudhary, A Gautam, R Kumar, GPS Raghava Computational peptidology, 143-157, 2015 | 165 | 2015 |
| TumorHoPe: A Database of Tumor Homing Peptides P Kapoor, H Singh, A Gautam, K Chaudhary, R Kumar, GPS Raghava PloS one 7 (4), e35187, 2012 | 149 | 2012 |
| DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data OB Poirion, Z Jing, K Chaudhary, S Huang, LX Garmire Genome medicine 13 (1), 1-15, 2021 | 147 | 2021 |
| CancerDR: cancer drug resistance database R Kumar*, K Chaudhary*, S Gupta*, H Singh, S Kumar, A Gautam, ... Scientific reports 3, 1445, 2013 | 146 | 2013 |
| In Silico Approach for Prediction of Antifungal Peptides P Agrawal, S Bhalla, K Chaudhary, R Kumar, M Sharma, GPS Raghava Frontiers in microbiology 9, 323, 2018 | 142 | 2018 |
Gajendra PS RaghavaIndraprastha Institute of Information Technology, IIIT-Delhi , IndiaVerified email at iiitd.ac.in
