A New Database for Drug Discovery Through Application of Data-Integration and Semantics

摘要: Biomedical databases are important in drug- discovery. Example applications are: disease-treatments, investi- gating side-effects of drugs, and identification similar research- efforts. A challenge construction biomedical concerns the complexities they describe: bio-medical data collected from a large number data-resources, where each data-resources targeted towards specific audience. What we observe is that current for semantic searches does not address needs Drug-discovery require multiple sources to be meaningfully unified/glued into database, ie, provide/give correct results complex queries. In practice established approaches ignore latter. Implication used drug-discovery. this paper latter issues. We present novel database which combines strategies model-unification 37 external databases, unwrapping BioPax formatted data-sets, data-normalization, inferences. From our empirical evaluation how approach captures effects causation pathways. Therefore provides support accurate Our new freely accessible through user-friendly search-interface at www.knittingTools.org.