| Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors H Li, ET Courtois, D Sengupta, Y Tan, KH Chen, JJL Goh, SL Kong, ... Nature genetics 49 (5), 708-718, 2017 | 340 | 2017 |
| Tumor-derived circulating endothelial cell clusters in colorectal cancer I Cima, SL Kong, D Sengupta, IB Tan, WM Phyo, D Lee, M Hu, C Iliescu, ... Science translational medicine 8 (345), 345ra89-345ra89, 2016 | 55 | 2016 |
| AutoImpute: Autoencoder based imputation of single-cell RNA-seq data D Talwar, A Mongia, D Sengupta, A Majumdar Scientific reports 8 (1), 1-11, 2018 | 43 | 2018 |
| dropClust: efficient clustering of ultra-large scRNA-seq data D Sinha, A Kumar, H Kumar, S Bandyopadhyay, D Sengupta Nucleic acids research 46 (6), e36-e36, 2018 | 42 | 2018 |
| Topological patterns in microRNA–gene regulatory network: studies in colorectal and breast cancer D Sengupta, S Bandyopadhyay Molecular bioSystems 9 (6), 1360-1371, 2013 | 32 | 2013 |
| FOCS: Fast overlapped community search S Bandyopadhyay, G Chowdhary, D Sengupta IEEE Transactions on Knowledge and Data Engineering 27 (11), 2974-2985, 2015 | 27 | 2015 |
| Participation of microRNAs in human interactome: extraction of microRNA–microRNA regulations D Sengupta, S Bandyopadhyay Molecular Biosystems 7 (6), 1966-1973, 2011 | 23 | 2011 |
| McImpute: Matrix completion based imputation for single cell RNA-seq data A Mongia, D Sengupta, A Majumdar Frontiers in genetics 10, 9, 2019 | 18 | 2019 |
| Weighted markov chain based aggregation of biomolecule orderings D Sengupta, U Maulik, S Bandyopadhyay IEEE/ACM transactions on computational biology and bioinformatics 9 (3), 924-933, 2012 | 17 | 2012 |
| Fast, scalable and accurate differential expression analysis for single cells D Sengupta, NA Rayan, M Lim, B Lim, S Prabhakar BioRxiv, 049734, 2016 | 16 | 2016 |
| Discovery of rare cells from voluminous single cell expression data A Jindal, P Gupta, D Sengupta Nature communications 9 (1), 1-9, 2018 | 15 | 2018 |
| CellAtlasSearch: a scalable search engine for single cells D Srivastava, A Iyer, V Kumar, D Sengupta Nucleic acids research 46 (W1), W141-W147, 2018 | 15 | 2018 |
| The molecular basis of loss of smell in 2019-nCoV infected individuals K Gupta, SK Mohanty, S Kalra, A Mittal, T Mishra, J Ahuja, D Sengupta, ... | 8 | 2020 |
| 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 | 8 | 2018 |
| Reformulated kemeny optimal aggregation with application in consensus ranking of microRNA targets D Sengupta, A Pyne, U Maulik, S Bandyopadhyay IEEE/ACM transactions on computational biology and bioinformatics 10 (3 …, 2013 | 8 | 2013 |
| deepmc: Deep matrix completion for imputation of single-cell rna-seq data A Mongia, D Sengupta, A Majumdar Journal of Computational Biology 27 (7), 1011-1019, 2020 | 6 | 2020 |
| Entropy steered Kendall's tau measure for a fair Rank Aggregation D Sengupta, U Maulik, S Bandyopadhyay 2011 2nd National Conference on Emerging Trends and Applications in Computer …, 2011 | 6 | 2011 |
| Integrative analysis and machine learning based characterization of single circulating tumor cells A Iyer, K Gupta, S Sharma, K Hari, YF Lee, N Ramalingam, YS Yap, ... Journal of clinical medicine 9 (4), 1206, 2020 | 5 | 2020 |
| A scoring scheme for online feature selection: simulating model performance without retraining D Sengupta, S Bandyopadhyay, D Sinha IEEE Transactions on Neural Networks and Learning Systems 28 (2), 405-414, 2016 | 5 | 2016 |
| MenstruLoss: Sensor For Menstrual Blood Loss Monitoring M Mukherjee, SA Naqvi, A Verma, D Sengupta, A Parnami Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous …, 2019 | 4 | 2019 |
