| Analysis of IS6110 insertion sites provide a glimpse into genome evolution of Mycobacterium tuberculosis T Roychowdhury, S Mandal, A Bhattacharya Scientific reports 5 (1), 12567, 2015 | 78 | 2015 |
| Temporal deep learning architecture for prediction of COVID-19 cases in India H Verma, S Mandal, A Gupta Expert Systems with Applications 195, 116611, 2022 | 59 | 2022 |
| Pattern of genomic variation in SARS-CoV-2 (COVID-19) suggests restricted nonrandom changes: Analysis using Shewhart control charts S Mandal, T Roychowdhury, A Bhattacharya Journal of Biosciences 46 (1), 11, 2021 | 20 | 2021 |
| Complex multifractal nature in Mycobacterium tuberculosis genome S Mandal, T Roychowdhury, K Chirom, A Bhattacharya, RK Brojen Singh Scientific reports 7 (1), 46395, 2017 | 11 | 2017 |
| Complexity in SARS-CoV-2 genome data: Price theory of mutant isolates S Mandal, RKS Singh, SK Sharma, MZ Malik, RKB Singh BioRxiv, 2020.05. 04.077511, 2020 | 6 | 2020 |
| Analysis of IS6110 insertion sites provide a glimpse into genome evolution of Mycobacterium tuberculosis. Sci Rep. 2015; 5: 12567 T Roychowdhury, S Mandal, A Bhattacharya | 6 | |
| Analysis of IS6110 insertion sites provide a glimpse into genome evolution of Mycobacterium tuberculosis. Sci Rep 5: 12567 T Roychowdhury, S Mandal, A Bhattacharya | 5 | 2015 |
| Stochastic method to control Mycobacterium tuberculosis epidemic S Mandal, SN Singh, MZ Malik, RKB Singh Computational Biology and Chemistry 87, 107250, 2020 | 2 | 2020 |
