Hiroyuki Kuwahara

The paper investigates the association between autozygosity and common diseases in multiple cohorts, finding significant links to twelve diseases and estimating autozygosity's contribution to type 2 diabetes cases.

This paper introduces RHSNet, a deep learning method that identifies and quantifies recombination hotspot determinants across species, sexes, and populations.

The paper identifies related 2D chemical fingerprints that reduce molecular similarity scores and suggests their exclusion for improved virtual screening.

The paper introduces omega, an RNA-seq quantification method that improves detection of transcriptional aberrations and reduces disease-gene search space using Mendelian disease cases.

PGCN jointly trains graph convolutional network embeddings and association prediction on a heterogeneous gene-disease network to prioritize candidate disease genes and recover missing associations in OMIM.