Aayush Grover

UniversalEPI: robust prediction of cell type-specific and differential chromatin interactions from DNA sequence and chromatin accessibility

UniversalEPI predicts cell type-specific and differential chromatin interactions using DNA sequence and ATAC-seq data, showing robust performance across cell types and conditions.

Chiron3D: an interpretable deep learning framework for understanding the DNA code of chromatin looping

Chiron3D predicts CTCF-mediated chromatin loops using a DNA-only model, revealing sequence determinants of loop strength and stability through interpretable deep learning.

Early feature extraction drives model performance in high-resolution chromatin accessibility prediction: A systematic evaluation of deep learning architectures

The paper evaluates deep learning architectures for high-resolution chromatin accessibility prediction, finding that early feature extraction, not downstream architecture, primarily determines performance.

SurvBoard: Standardised Benchmarking for Multi-omics Cancer Survival Models

SurvBoard benchmarks multi-omics cancer survival models, showing statistical models outperform deep learning in calibration and that pan-cancer training improves performance.

ProtFinder: finding subcellular locations of proteins using protein interaction networks

ProtFinder predicts multiple subcellular locations of proteins using protein interaction networks and biological priors, achieving high accuracy on held-out data.