|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
|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
|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
|Mapping the human genetic architecture of COVID-19|
|More is better: recent progress in multi-omics data integration methods|
S Huang, K Chaudhary, LX Garmire
Frontiers in genetics 8, 84, 2017
|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
|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
|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
|CPPsite: a curated database of cell penetrating peptides|
A Gautam, H Singh, A Tyagi, K Chaudhary, R Kumar, P Kapoor, ...
Database 2012, bas015, 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
|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), 1-19, 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
|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
|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
|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
|CancerDR: cancer drug resistance database|
R Kumar*, K Chaudhary*, S Gupta*, H Singh, S Kumar, A Gautam, ...
Scientific reports 3, 1445, 2013
|An in silico platform for predicting, screening and designing of antihypertensive peptides|
R Kumar*, K Chaudhary*, JS Chauhan*, G Nagpal*, R Kumar*, M Sharma, ...
Scientific reports 5, 12512, 2015
|Peptide toxicity prediction|
S Gupta, P Kapoor, K Chaudhary, A Gautam, R Kumar, GPS Raghava
Computational peptidology, 143-157, 2015
|Hemolytik: a database of experimentally determined hemolytic and non-hemolytic peptides|
A Gautam*, K Chaudhary*, S Singh*, A Joshi, P Anand, A Tuknait, ...
Nucleic acids research 42 (D1), D444-D449, 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