Anupama Jha

scWeave translates between gene expression and chromatin structure at single-cell resolution using deep learning, improving cross-modality inference across multiple datasets.

HiCFoundation is a Hi-C-based model that integrates chromatin structure with epigenomic regulation across species, improving analysis of 3D genome architecture and single-cell data.

This paper introduces TrASPr+BOS, a generative AI model for predicting and designing RNA to achieve tissue-specific splicing outcomes, showing improved performance over existing methods.

TwinC predicts trans-chromosomal DNA contacts using DNA sequence and provides insights into their functional mechanisms.

Puget predicts gene expression across cell types using sequence and Hi-C data, improving generalization to new biosamples and species.

Evo2HiC improves prediction of 3D genome architecture and epigenomic signals by integrating DNA sequences and Hi-C data, achieving better accuracy and computational efficiency.

This paper introduces LSV-seq, a targeted RNA-seq method optimized with machine learning to detect alternative splicing with high sensitivity and low sequencing depth.

This paper introduces fibertools, a toolkit for DNA-m6A detection and integration of long-read genetic and epigenetic data, achieving high accuracy and speed.

Capricorn enhances low-coverage Hi-C contact matrices using a multi-view diffusion model to improve loop detection accuracy across cell lines and chromosomes.

The paper introduces MAJIQ v2, a tool for analyzing RNA splicing in large and heterogeneous RNA-seq datasets, showing improved performance over existing methods and applying it to brain subregion-specific splicing analysis.

MOCCASIN corrects known and unknown confounders in RNA splicing analysis using synthetic and real data from TARGET and ENCODE.

This study identifies pleiotropic genetic loci shared between Alzheimer's disease and cardiometabolic traits using bivariate GWAS and colocalization analysis.

The paper extends the Integrated Gradients method for better interpretability of deep learning models in genomics by incorporating nonlinear paths and alternative baselines.

The paper identifies antagonistic splicing regulation between CELF2 and RBFOX family proteins in human T cells and shows evolutionary conservation across species.

This paper improves AS prediction models by integrating diverse data sources and introduces a new target function that enhances model accuracy.