Ben D Coleman

The paper compares semantic similarity algorithms for differential diagnosis and clustering using HPO, finding Phenomizer performs best for ranking and non-normalized algorithms for clustering.

The paper presents a large-scale corpus of standardized phenopackets for genomic diagnostics and discovery, curated from literature and useful for software testing and genotype-phenotype analysis.

The paper introduces a degree-aware edge sampling method to reduce performance inflation in biomedical graph representation learning caused by imbalanced edge degree distributions.

The paper uses word embeddings from PubMed to predict which protein kinases are relevant for treating specific cancers using machine learning.