Debarka Sengupta
Debarka Sengupta
Asst. Prof., IIIT-Delhi & (Adj.) Assoc. Prof., QUT, Brisbane
Verified email at - Homepage
Cited by
Cited by
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
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
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
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
Topological patterns in microRNA–gene regulatory network: studies in colorectal and breast cancer
D Sengupta, S Bandyopadhyay
Molecular bioSystems 9 (6), 1360-1371, 2013
FOCS: Fast overlapped community search
S Bandyopadhyay, G Chowdhary, D Sengupta
IEEE Transactions on Knowledge and Data Engineering 27 (11), 2974-2985, 2015
Participation of microRNAs in human interactome: extraction of microRNA–microRNA regulations
D Sengupta, S Bandyopadhyay
Molecular Biosystems 7 (6), 1966-1973, 2011
McImpute: Matrix completion based imputation for single cell RNA-seq data
A Mongia, D Sengupta, A Majumdar
Frontiers in genetics 10, 9, 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
Fast, scalable and accurate differential expression analysis for single cells
D Sengupta, NA Rayan, M Lim, B Lim, S Prabhakar
BioRxiv, 049734, 2016
Discovery of rare cells from voluminous single cell expression data
A Jindal, P Gupta, D Sengupta
Nature communications 9 (1), 1-9, 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
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, ...
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
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
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
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
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
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
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
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