| Prediction of continuous B‐cell epitopes in an antigen using recurrent neural network S Saha, GPS Raghava Proteins: Structure, Function, and Bioinformatics 65 (1), 40-48, 2006 | 1510 | 2006 |
| AlgPred: prediction of allergenic proteins and mapping of IgE epitopes S Saha, GPS Raghava Nucleic acids research 34 (suppl_2), W202-W209, 2006 | 704 | 2006 |
| BcePred: prediction of continuous B-cell epitopes in antigenic sequences using physico-chemical properties S Saha, GPS Raghava International conference on artificial immune systems, 197-204, 2004 | 516 | 2004 |
| Bcipep: a database of B-cell epitopes S Saha, M Bhasin, GPS Raghava BMC genomics 6 (1), 1-7, 2005 | 240 | 2005 |
| Support Vector Machine-based method for predicting subcellular localization of mycobacterial proteins using evolutionary information and motifs M Rashid, S Saha, GPS Raghava BMC bioinformatics 8, 1-9, 2007 | 156 | 2007 |
| VICMpred: an SVM-based method for the prediction of functional proteins of Gram-negative bacteria using amino acid patterns and composition S Saha, GPS Raghava Genomics, proteomics & bioinformatics 4 (1), 42-47, 2006 | 156 | 2006 |
| Metagenomic surveys of gut microbiota RS Mandal, S Saha, S Das Genomics, proteomics & bioinformatics 13 (3), 148-158, 2015 | 133 | 2015 |
| Prediction methods for B-cell epitopes S Saha, GPS Raghava Immunoinformatics: Predicting Immunogenicity In Silico, 387-394, 2007 | 121 | 2007 |
| Prediction of interactions between viral and host proteins using supervised machine learning methods RK Barman, S Saha, S Das PloS one 9 (11), e112034, 2014 | 81 | 2014 |
| Enhanced energy metabolism contributes to the extended life span of calorie-restricted Caenorhabditis elegans Y Yuan, CS Kadiyala, TT Ching, P Hakimi, S Saha, H Xu, C Yuan, ... Journal of Biological Chemistry 287 (37), 31414-31426, 2012 | 76 | 2012 |
| LncRBase: an enriched resource for lncRNA information S Chakraborty, A Deb, RK Maji, S Saha, Z Ghosh PloS one 9 (9), e108010, 2014 | 73 | 2014 |
| BTXpred: prediction of bacterial toxins S Saha, GPS Raghava In silico biology 7 (4-5), 405-412, 2007 | 72 | 2007 |
| Machine-learning techniques for the prediction of protein–protein interactions D Sarkar, S Saha Journal of biosciences 44 (4), 104, 2019 | 63 | 2019 |
| Prediction of neurotoxins based on their function and source S Saha, GPS Raghava In silico biology 7 (4-5), 369-387, 2007 | 59 | 2007 |
| piRNAQuest: searching the piRNAome for silencers A Sarkar, RK Maji, S Saha, Z Ghosh BMC genomics 15 (1), 1-17, 2014 | 55 | 2014 |
| Survey of drug resistance associated gene mutations in Mycobacterium tuberculosis, ESKAPE and other bacterial species A Ghosh, S N, S Saha Scientific reports 10 (1), 8957, 2020 | 44 | 2020 |
| Effect of diet on enzyme profile, biochemical changes and in sacco degradability of feeds in the rumen of buffalo DN Kamra, S Saha, N Bhatt, LC Chaudhary, N Agarwal ASIAN AUSTRALASIAN JOURNAL OF ANIMAL SCIENCES 16 (3), 374-379, 2003 | 38 | 2003 |
| SUMOylation pathway alteration coupled with downregulation of SUMO E2 enzyme at mucosal epithelium modulates inflammation in inflammatory bowel disease SA Mustfa, M Singh, A Suhail, G Mohapatra, S Verma, D Chakravorty, ... Open Biology 7 (6), 170024, 2017 | 37 | 2017 |
| R ing‐H ydroxylating O xygenase database: a database of bacterial aromatic ring‐hydroxylating oxygenases in the management of bioremediation and biocatalysis of aromatic compounds J Chakraborty, T Jana, S Saha, TK Dutta Environmental microbiology reports 6 (5), 519-523, 2014 | 36 | 2014 |
| High throughput screen identifies small molecule inhibitors specific for Mycobacterium tuberculosis phosphoserine phosphatase G Arora, P Tiwari, RS Mandal, A Gupta, D Sharma, S Saha, R Singh Journal of Biological Chemistry 289 (36), 25149-25165, 2014 | 36 | 2014 |
Gajendra PS RaghavaIndraprastha Institute of Information Technology, IIIT-Delhi , IndiaVerified email at iiitd.ac.in
