Emanuele Cavalleri

Plausibility-Driven Prioritization of Candidate Biomedical Annotations

This work proposes a framework that leverages bioKGs to estimate the plausibility of candidate annotations and guide expert curation, and introduces a family of plausibility measures that combine classifier confidence, classifier reliability, and the semantic context provided by alternative relationships involving the same pair of biological entities.