Debarka Sengupta

发表

ROSeq: Modeling expression ranks for noise-tolerant differential expression analysis of scRNA-Seq data

Ujjwal Maulik, Abhik Ghosh, Krishan Gupta, 2018 .

McImpute: Matrix completion based imputation for single cell RNA-seq data

Angshul Majumdar, Debarka Sengupta, Aanchal Mongia, 2018 .

ROSeq: A rank based approach to modeling gene expression in single cells

Manan Lalit, Debarka Sengupta, Abhik Ghosh, 2018 .

Pathway Informatics

Debarka Sengupta, Sarita Poonia, Smriti Chawla, 2019, Encyclopedia of Bioinformatics and Computational Biology.

deepMc: deep Matrix Completion for imputation of single cell RNA-seq data

Angshul Majumdar, Debarka Sengupta, Aanchal Mongia, 2018, bioRxiv.

Linear time identification of local and global outliers

Jayadeva, Debarka Sengupta, Aashi Jindal, 2020 .

Structure-Aware Principal Component Analysis for Single-Cell RNA-seq Data

Sanghamitra Bandyopadhyay, Debarka Sengupta, Snehalika Lall, 2018, J. Comput. Biol..

deepMc: Deep Matrix Completion for Imputation of Single-Cell RNA-seq Data.

Angshul Majumdar, Debarka Sengupta, Aanchal Mongia, 2020, Journal of computational biology : a journal of computational molecular cell biology.

ParSel: Parallel Selection of Micro‐RNAs for Survival Classification in Cancers

Sanghamitra Bandyopadhyay, Debarka Sengupta, Debajyoti Sinha, 2017, Molecular informatics.

Improved dropClust R package with integrative analysis support for scRNA-seq data

Debarka Sengupta, Sanghamitra Bandyopadhyay, Debajyoti Sinha, 2019, Bioinform..

SelfE: Gene Selection via Self-Expression for Single-Cell Data

Debarka Sengupta, Angshul Majumdar, Priyadarshini Rai, 2020, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

McImpute: Matrix Completion Based Imputation for Single Cell RNA-seq Data

Angshul Majumdar, Debarka Sengupta, Aanchal Mongia, 2018, bioRxiv.

MenstruLoss: Sensor For Menstrual Blood Loss Monitoring

Debarka Sengupta, Aman Parnami, Manideepa Mukherjee, 2019, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

Fast, scalable and accurate differential expression analysis for single cells

Debarka Sengupta, Nirmala Arul Rayan, Shyam Prabhakar, 2016, bioRxiv.

Hide and Seek: Outwitting Community Detection Algorithms

Debarka Sengupta, Shravika Mittal, Tanmoy Chakraborty, 2021, IEEE Transactions on Computational Social Systems.

Machine-OlF-Action: a unified framework for developing and interpreting machine-learning models for chemosensory research

Krishan Gupta, Gaurav Ahuja, Debarka Sengupta, 2021, Bioinform..

ComBI: Compressed Binary Search Tree for Approximate k-NN Searches in Hamming Space

Jayadeva, Debarka Sengupta, Aashi Jindal, 2021, Big Data Res..

AutoImpute: Autoencoder based imputation of single-cell RNA-seq data

Angshul Majumdar, Debarka Sengupta, Aanchal Mongia, 2018, Scientific Reports.

Integrative Analysis and Machine Learning based Characterization of Single Circulating Tumor Cells

Debarka Sengupta, Tuan Zea Tan, Arvind Iyer, 2019, bioRxiv.

Discovery of rare cells from voluminous single cell expression data

Jayadeva, Debarka Sengupta, Aashi Jindal, 2018, Nature Communications.

Participation of microRNAs in human interactome: extraction of microRNA-microRNA regulations.

Sanghamitra Bandyopadhyay, Debarka Sengupta, S. Bandyopadhyay, 2011, Molecular bioSystems.

Entropy steered Kendall's tau measure for a fair Rank Aggregation

Ujjwal Maulik, Sanghamitra Bandyopadhyay, Debarka Sengupta, 2011, 2011 2nd National Conference on Emerging Trends and Applications in Computer Science.

A novel measure for evaluating an ordered list: application in microRNA target prediction

Ujjwal Maulik, Sanghamitra Bandyopadhyay, Debarka Sengupta, 2010 .

dropClust: efficient clustering of ultra-large scRNA-seq data

Sanghamitra Bandyopadhyay, Debarka Sengupta, Debajyoti Sinha, 2017, bioRxiv.

Integrative Analysis and Machine Learning based Characterization of Single Circulating Tumor Cells

Krishan Gupta, Debarka Sengupta, Shreya Sharma, 2020, Journal of clinical medicine.