Data Mining in Biomedicine Using Ontologies [Repost]

Ontologies (sets of vocabulary terms whose meanings and relations with other terms are explicitly stated) are used in biomedical research for clustering and interpretation of biological data, protein classification, gene and pathway prediction, text mining, and connecting different databases. This book introduces emerging developments in bio-ontologies, focusing on algorithms and methodologies rather than on application domains. Examples at the molecular to clinical level represent areas of description logic, probability, and fuzzy logic. The book also covers data mining approaches, such as unsupervised learning, classification, and rule mining. Each self-contained chapter begins with a problem definition and historical perspective, then presents a mathematical or computational formulation of the problem, and describes computational methods and performance results. There are also details on technological infrastructure for bio-ontologies. About 160 illustrations support key topics. The book can be used as a text for upper undergraduate and beginning graduate courses in bioinformatics and medical informatics. It will also be useful to postdoctoral fellows, professional practitioners, and bioinformatics and medical informatics experts.