Fernando Zhapa-Camacho

This paper introduces an inductive method to predict gene-disease associations using phenotype ontologies, achieving performance comparable to transductive methods while generalizing to novel diseases.

This paper introduces PU-GO, a method for protein function prediction that addresses the positive-unlabeled ranking problem using ontology-based priors, showing improved performance on benchmark datasets.

This paper introduces EmbedPVP, a method that prioritizes genomic variants in genetic diseases by integrating genomic data with clinical phenotypes and background knowledge.