| Structure-aware principal component analysis for single-cell RNA-seq data S Lall, D Sinha, S Bandyopadhyay, D Sengupta Journal of Computational Biology 25 (12), 1365-1373, 2018 | 44 | 2018 |
| Stable feature selection using copula based mutual information S Lall, D Sinha, A Ghosh, D Sengupta, S Bandyopadhyay Pattern Recognition 112, 107697, 2021 | 42 | 2021 |
| Predicting potential drug targets and repurposable drugs for covid-19 via a deep generative model for graphs S Ray, S Lall, A Mukhopadhyay, S Bandyopadhyay, A Schönhuth arXiv preprint arXiv:2007.02338, 2020 | 16 | 2020 |
| CODC: a Copula-based model to identify differential coexpression S Ray, S Lall, S Bandyopadhyay NPJ systems biology and applications 6 (1), 20, 2020 | 13 | 2020 |
| Multi-agent reinfocement learning for stochastic power management in cognitive radio network S Lall, AK Sadhu, A Konar, KK Mallik, S Ghosh 2016 International Conference on Microelectronics, Computing and …, 2016 | 13 | 2016 |
| sc-REnF: An entropy guided robust feature selection for single-cell RNA-seq data S Lall, A Ghosh, S Ray, S Bandyopadhyay Briefings in Bioinformatics 23 (2), bbab517, 2022 | 10 | 2022 |
| RgCop-A regularized copula based method for gene selection in single-cell RNA-seq data S Lall, S Ray, S Bandyopadhyay PLoS computational biology 17 (10), e1009464, 2021 | 9 | 2021 |
| A deep integrated framework for predicting SARS-CoV2–human protein-protein interaction S Ray, S Lall, S Bandyopadhyay IEEE Transactions on Emerging Topics in Computational Intelligence 6 (6 …, 2022 | 7 | 2022 |
| EEG-based mind driven type writer by fuzzy radial basis function neural classifier S Lall, A Saha, A Konar, M Laha, AL Ralescu, K kumar Mallik, AK Nagar 2016 International Joint Conference on Neural Networks (IJCNN), 1076-1082, 2016 | 7 | 2016 |
| A copula based topology preserving graph convolution network for clustering of single-cell RNA-seq data S Lall, S Ray, S Bandyopadhyay PLOS Computational Biology 18 (3), e1009600, 2022 | 5 | 2022 |
| Generating realistic cell samples for gene selection in scRNA-seq data: A novel generative framework S Lall, S Ray, S Bandyopadhyay bioRxiv, 2021.04. 29.441920, 2021 | 4 | 2021 |
| Deep variational graph autoencoders for novel host-directed therapy options against COVID-19 S Ray, S Lall, A Mukhopadhyay, S Bandyopadhyay, A Schönhuth Artificial Intelligence in Medicine 134, 102418, 2022 | 3 | 2022 |
| LSH-GAN enables in-silico generation of cells for small sample high dimensional scRNA-seq data S Lall, S Ray, S Bandyopadhyay Communications Biology 5 (1), 577, 2022 | 3 | 2022 |
| Innovative molecular testing strategies for adjunctive investigations in hemostasis and thrombosis E Ghorbanpour, D Lillicrap Seminars in thrombosis and hemostasis 45 (07), 751-756, 2019 | 3 | 2019 |
| sc-REnF: An entropy guided robust feature selection for clustering of single-cell rna-seq data S Lall, A Ghosh, S Ray, S Bandyopadhyay bioRxiv, 2020.10. 10.334573, 2020 | 2 | 2020 |
| An l1-Norm regularized copula based feature selection S Lall, S Bandyopadhyay Proceedings of the 2019 3rd International Symposium on Computer Science and …, 2019 | 2 | 2019 |
| An improved measure for data clustering in high dimensional space S Lall, R Lahiri, A Konar, S Ghosh 2016 International Conference on Microelectronics, Computing and …, 2016 | 1 | 2016 |
| Algorithms for Feature Selection S Lall Indian Statistical Institute, Kolkata, 2022 | | 2022 |
| LSH-GAN: in-silico generation of cells for small sample high dimensional scRNA-seq data S Lall, S Ray, S Bandyopadhyay | | 2021 |
| Biological and cognitive communication using machine intelligence techniques S Lall Jadavpur University, Kolkata, West Bengal, 2016 | | 2016 |
Sanghamitra BandyopadhyayProfessor of Computer Science, Indian Statistical Institute, Kolkata, IndiaVerified email at isical.ac.in
