The volume of literature data increases tremendously which makes it inevitable to apply text mining methods to retrieve, manage and analyse the information. Ontologies represent information semantically by describing the knowledge as a set of concepts within specific domains and the relationships between these concepts. Text mining profits from biomedical ontologies in different tasks, especially in mapping ontology concepts to terms in text (e.g. named entity recognition) since they provide domain knowledge. Ontology development profits from automated text mining methods since publications contain references to existing terms and concepts.
In this talk, I will discuss different approaches in which ontologies and text-mining methods have been used in biomedicine with a special focus on infectious diseases. More specifically, I will present on the development of PathoPhenoDB - a database containing pathogen-disease phenotype relations for supporting infectious disease research; use of text mining for expanding the Disease Ontology and use of ontologies and text mined data for predicting host-pathogen interactions.
Building 9 - Lecture hall 2
11:00 - 11:35