Automatic Extraction of Information about the Molecular Interactions in Biological Pathways from Texts Based on Ontology and Semantic Processing

We develop a framework using ontology inference and semantic processing techniques to help biologists to extract knowledge directly from a large scale of biological literature in NCBI PubMed. The system integrated various sharable thesauri of WordNet, MeSH (Medical Subject Heading), and GO (Gene ontology) to support the automatic semantic annotation and analysis. The natural language processing and semantic processing are facilitated by the ontological inference, and the system could automatically extract the correct molecular interactions from the complex sentences in an abstract automatically. It facilitates the biologists not only to save time and efforts to construct and analyze biological pathways, but also to discover the novel molecular interactions by comparing the information extracted from the literature with that in such existing pathway database as KEGG. We evaluated the system performance based on the pathways in Apoptosis domain.