- author = "Rita T. Sousa and Sara Silva and Catia Pesquita",
- title = "Explaining Protein-Protein Interaction Predictions with Genetic Programming",
- booktitle = "Evostar 2022 Late breaking abstracts",
- year = "2022",
- editor = "A. M. Mora and A. I. Esparcia-Alcazar",
- pages = "30--33",
- address = "Madrid",
- month = "20-22 " # apr,
- organisation = "Species",
- keywords = "genetic algorithms, genetic programming, XAI, Explainable Artificial Intelligence, Knowledge Graph, Protein-Protein Interaction Prediction",
-
URL = "
https://arxiv.org/abs/2208.00555",
- size = "4 pages",
- abstract = "Explainability is crucial to support the adoption of machine learning as a tool for scientific discovery. In the biomedical domain, ontologies and knowledge graphs are a unique opportunity to explore domain knowledge, but most knowledge graph-based approaches employ graph embeddings, which are not explainable. However, when the prediction target is finding a relation between two entities represented in the graph, such as in the case of protein-protein interaction prediction, semantic similarity presents itself as a natural explanatory mechanism. This work uses genetic programming over a set of semantic similarity values, each describing a semantic aspect represented in the knowledge graph, to generate global and interpretable explanations for protein-protein interaction prediction. Our experiments reveal that genetic programming algorithms coupled with semantic similarity produce global models relevant to understanding the biological phenomena",
-
notes = "https://string-db.org Gene Ontology GO.
published 1 Aug 2022 https://www.evostar.org/2022/late-breaking-abstracts/",
