Author

Sanghamitra Bandyopadhyay

Indian Statistical InstituteORCID 0000-0002-1490-3599Unclaimed ORCID profile · follow to get notified of new preprints

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Sanghamitra Bandyopadhyay1 paper · Indian Statistical Institute

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Assay:CITE-seq· 1 Research Focus:clustering· 1 Research Focus:drug response / drug discovery· 1 Approach:graph neural network· 1 Assay:scRNA-seq· 1 Approach:variational autoencoder· 1

Frequent Co-Authors

Ashmita DeyiD· 2 shared Aishik Chanda· 1 shared Mrittika ChakrabortyiD· 1 shared Rangan DasiD· 1 shared Ujjwal Maulik· 1 shared Utsav B. MaulikiD· 1 shared

Activity by Year

2024-2026 · peak 1/yr

Co-Author Collaboration Network

6 top collaborators · 9 mutual co-author links

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2 shared papersBioinformatics

Ashmita DeyORCID

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2 papers

Recent·Most cited

BioinformaticsbioRxivPreprintMay 11, 2026

SRSA-VAE: Self-Attention-Based Feature Learning for Single-Cell Multimodal Clustering

Rangan Das, Ashmita Dey, Ujjwal Maulik, Sanghamitra Bandyopadhyay

SRSA-VAE improves single-cell clustering by using self-attention and residual connections, outperforming existing methods on scRNA-seq and CITE-seq data.

DOI 10.64898/2026.05.06.723212

scRNA-seqCITE-seq

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BioinformaticsbioRxivPreprintSep 24, 2024

BRDKRM: An Explainable Framework for Disease Modifying Drug Identification

Aishik Chanda, Ashmita Dey, Mrittika Chakraborty…·Sanghamitra Bandyopadhyay

BRDKRM classifies drugs as disease-modifying or symptomatic using a graph-based framework, achieving 86.78% accuracy and identifying 530 genes involved in both categories.

DOI 10.1101/2024.09.24.614653

graph neural networkdrug response / drug discovery

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