Sumanta Ray

A Graph-Attentive GAN for Rare-Cell-Aware Single-Cell RNA-Seq Data Generation

GARAGE generates realistic synthetic scRNA-seq data by addressing high-dimensional, small-sample and rare-cell challenges using graph attention and adversarial generation.

RareCapsNet: An explainable capsule networks enable robust discovery of rare cell populations from large-scale single-cell transcriptomics

RareCapsNet identifies rare cell populations in single-cell RNA-seq data using capsule networks, outperforming existing methods in specificity and selectivity.

Generative Reconstruction of Unobserved Cellular Dynamics using Single-Cell Transcriptomic Trajectories

GRAIL reconstructs intermediate cellular states between two developmental stages using a combination of LSH-based cell selection, latent space interpolation, and GAN-based gene expression generation.

A mathematical framework for human neutrophil state transitions inferred from single-cell RNA sequence data

The paper develops a mathematical model of human neutrophil maturation using single-cell RNA-seq data, identifying key transition states and rates.

Integration of Biological Data via NMF for Identification of Human Disease-Associated Gene Modules through Multi-label Classification.

This paper uses NMF to integrate PPI and Gene Ontology data to identify disease-associated gene modules and predict 3131 gene-disease associations.

A copula based topology preserving graph convolution network for clustering of single-cell RNA-seq data

This paper introduces sc-CGconv, a graph convolution network for single-cell RNA-seq clustering that improves homogeneity by preserving cell-to-cell variability and handling technical noise.

RgCop-A regularized copula based method for gene selection in single cell rna-seq data

RgCop improves gene selection in scRNA-seq by using copula correlation with l1 regularization, enhancing clustering and classification performance.

Identification of key immune regulatory genes in HIV-1Progression

The paper identifies key immune regulatory genes in HIV-1 progression by analyzing microarray data and examining module preservation across infection stages.

Generating realistic cell samples for gene selection in scRNA-seq data: A novel generative framework

The paper introduces LSH-GAN, a generative framework to improve gene selection in high-dimensional, small-sample scRNA-seq data by generating realistic samples.

Discovering Key Transcriptomic Regulators in Pancreatic Ductal Adenocarcinoma using Dirichlet Process Gaussian Mixture Model

This paper identifies key transcriptomic regulators in pancreatic ductal adenocarcinoma using a Dirichlet process Gaussian mixture model and time-series gene expression data.

sc-REnF: An entropy guided robust feature selection for clustering of single-cell rna-seq data

sc-REnF improves single-cell RNA-seq clustering by selecting robust genes using entropy-based methods, reducing technical noise impact and enhancing sub-type detection.

MarkerCapsule: Explainable Single Cell Typing using Capsule Networks

MarkerCapsule improves single cell typing by using capsule networks, requiring less labeled data and providing biologically meaningful gene activity patterns, while outperforming existing methods.

CODC: A copula based model to identify differential coexpression.

The paper introduces CODC, a copula-based model to identify differential coexpression between gene pairs across two conditions, showing improved performance on TCGA RNA-Seq data.