Sanghamitra Bandyopadhyay

The paper investigates how HDAC and LSD1 inhibition alters chromatin accessibility and nucleosome positioning in cancer cells, revealing a JunB-mediated mechanism that highlights therapeutic vulnerabilities.

RSTG generates high-quality spatial transcriptomics data using a beta divergence-based autoencoder, showing robustness to noise and outliers across multiple datasets.

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.

ROSeq provides a robust differential expression analysis method for scRNA-seq data by modeling expression ranks, showing improved noise tolerance and scalability.

S-conLSH maps noisy long reads to reference genomes without alignment, achieving high sensitivity and speed.

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 uses k-mer distributions to predict the geographical origin of SARS-CoV-2 sequences with over 90% accuracy by under-sampling imbalanced data.

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.

The paper analyzes early SARS-CoV-2 genomic sequences to trace their temporal changes and finds that n-mer distributions can predict collection timelines with 78% accuracy.

This paper introduces conLSH, a contextual Locality Sensitive Hashing algorithm for aligning noisy SMRT reads to a reference genome, showing improved speed and memory efficiency compared to existing methods.

dropClust2 is an R package that improves the efficiency and resource usage of single cell RNA-seq clustering with a new batch effect removal algorithm.

dropClust efficiently clusters ultra-large scRNA-seq data using Locality Sensitive Hashing, outperforming existing methods in speed and accuracy.