Snehalika Lall

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

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.

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

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

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.

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.