Matthew C. Hill

scHiR learns joint representations from paired scRNA-seq and scHi-C data, improving clustering and trajectory inference while recovering input signals.

NanoLoop predicts genome-wide chromatin loops using Nanopore sequencing data, identifying methylation patterns that influence loop types and reveal new long-range interactions.

FusNet predicts protein-mediated chromatin loops using genome sequence, open chromatin, and ChIP-seq data, showing high consistency with Hi-C and associations with regulatory functions and disease risk variants.

The paper applies transfer learning to predict network biology features across different biological contexts.

The paper uses machine learning to analyze myocardial T1 time in UK Biobank data, identifying genetic loci and pathways associated with interstitial fibrosis and related cardiovascular diseases.

Spateo provides a framework for spatiotemporal modeling of single-cell spatial transcriptomics data to study cell-cell interactions and organ development in 3D space.

This study constructs a 3D genome atlas of genetic variants and their pathological effects using a scoring algorithm and a web-based database.

This study identifies transcriptional and epigenetic mechanisms driving endothelial diversity across organs in mice, revealing conserved gene regulatory networks in the brain vasculature.

LoopPredictor predicts enhancer-promoter loops in cell types lacking long-range contact maps using ensemble machine learning and multi-omics features.

The paper generates Hi-C data from human cardiac tissue and develops a method to identify regulatory interactions, showing how disease variants intersect with these interactions to prioritize causative genes.