| Attention based multi-modal new product sales time-series forecasting V Ekambaram, K Manglik, S Mukherjee, SSK Sajja, S Dwivedi, V Raykar Proceedings of the 26th ACM SIGKDD international conference on knowledge …, 2020 | 82 | 2020 |
| Computational analysis of proteome of H5N1 avian influenza virus to define T cell epitopes with vaccine potential R Parida, MS Shaila, S Mukherjee, NR Chandra, R Nayak Vaccine 25 (43), 7530-7539, 2007 | 49 | 2007 |
| ABS–Scan: In silico alanine scanning mutagenesis for binding site residues in protein–ligand complex P Anand, D Nagarajan, S Mukherjee, N Chandra F1000Research 3, 214, 2014 | 47 | 2014 |
| PLIC: protein–ligand interaction clusters P Anand, D Nagarajan, S Mukherjee, N Chandra Database 2014, bau029, 2014 | 45 | 2014 |
| Structural Annotation of Mycobacterium tuberculosis Proteome P Anand, S Sankaran, S Mukherjee, K Yeturu, R Laskowski, A Bhardwaj, ... PloS one 6 (10), e27044, 2011 | 44 | 2011 |
| Tiny time mixers (ttms): Fast pre-trained models for enhanced zero/few-shot forecasting of multivariate time series V Ekambaram, A Jati, P Dayama, S Mukherjee, N Nguyen, WM Gifford, ... Advances in Neural Information Processing Systems 37, 74147-74181, 2024 | 24 | 2024 |
| Explainable AI based interventions for pre-season decision making in fashion retail S Sajja, N Aggarwal, S Mukherjee, K Manglik, S Dwivedi, V Raykar Proceedings of the 3rd ACM India joint international conference on data …, 2021 | 24 | 2021 |
| Role of genetic heterogeneity in determining the epidemiological severity of H1N1 influenza N Sambaturu, S Mukherjee, M López-García, C Molina-París, GI Menon, ... PLoS computational biology 14 (3), e1006069, 2018 | 21 | 2018 |
| HLaffy: estimating peptide affinities for Class-1 HLA molecules by learning position-specific pair potentials S Mukherjee, C Bhattacharyya, N Chandra Bioinformatics 32 (15), 2297-2305, 2016 | 20 | 2016 |
| A multi-level multi-scale approach to study essential genes in Mycobacterium tuberculosis S Ghosh, P Baloni, S Mukherjee, P Anand, N Chandra BMC Systems Biology 7, 1-21, 2013 | 19 | 2013 |
| Deciphering complex patterns of class‐I HLA–peptide cross‐reactivity via hierarchical grouping S Mukherjee, J Warwicker, N Chandra Immunology and cell biology 93 (6), 522-532, 2015 | 11 | 2015 |
| Resource Demand Prediction for Distributed Service Network S Mukherjee, K Narayanam, A Singhee, N Aggarwal US Patent App. 15/903,267, 2019 | 10 | 2019 |
| Resource position planning for distributed demand satisfaction K Narayanam, A Singhee, S Mukherjee, F Barahona, JPM Goncalves US Patent 10,805,382, 2020 | 9 | 2020 |
| TsSHAP: Robust model agnostic feature-based explainability for time series forecasting VC Raykar, A Jati, S Mukherjee, N Aggarwal, K Sarpatwar, ... arXiv preprint arXiv:2303.12316, 2023 | 6 | 2023 |
| Hierarchical proxy modeling for improved hpo in time series forecasting A Jati, V Ekambaram, S Pal, B Quanz, WM Gifford, P Harsha, S Siegel, ... Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and …, 2023 | 5 | 2023 |
| Customizing agricultural practices to maximize crop yield SN Marvaniya, SR Godbole, S Mukherjee, VC Raykar US Patent 11,645,308, 2023 | 5 | 2023 |
| Grouping of large populations into few CTL immune ‘response‐types’ from influenza H1N1 genome analysis S Mukherjee, N Chandra Clinical & translational immunology 3 (8), e24, 2014 | 5 | 2014 |
| Rationalization and prediction of drug resistant mutations in targets for clinical anti-tubercular drugs J Padiadpu, S Mukherjee, N Chandra Journal of Biomolecular Structure and Dynamics 31 (1), 44-58, 2013 | 5 | 2013 |
| Contextual anomaly detection across assets K Kulkarni, PV Seshadri, S Mukherjee, S Dwivedi US Patent 11,271,957, 2022 | 4 | 2022 |
| AI Explainability 360 toolkit for time-series and industrial use cases G Ganapavarapu, S Mukherjee, N Martinez Gil, K Sarpatwar, ... Proceedings of the 29th ACM SIGKDD conference on knowledge discovery and …, 2023 | 3 | 2023 |
Nagasuma ChandraIndian Institute of ScienceBestätigte E-Mail-Adresse bei iisc.ac.in
