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• W1516264322 abstract "AbstractThe identification and characterization of important roles microRNAs (miRNAs) played in human cancer is an increasingly active area in medical informatics. In particular, the prediction of miRNA target genes remains a challenging task to cancer researchers. Current efforts have focused on manual knowledge acquisition from existing miRNA databases, which is time-consuming, error-prone, and subject to biologists’ limited prior knowledge. Therefore, an effective knowledge acquisition has been inhibited. We propose a computing framework based on the Ontology for MicroRNA Target Prediction (OMIT), the very first ontology in miRNA domain. With such formal knowledge representation, it is thus possible to facilitate knowledge discovery and sharing from existing sources. Consequently, the framework aims to assist biologists in unraveling important roles of miRNAs in human cancer, and thus to help clinicians in making sound decisions when treating cancer patients.KeywordsResource Description FrameworkMicroRNA Target PredictionFormal Knowledge RepresentationDirected Acyclic Graph ModelLimited Prior KnowledgeThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves." @default.
• W1516264322 created "2016-06-24" @default.
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• W1516264322 date "2010-01-01" @default.
• W1516264322 modified "2023-09-26" @default.
• W1516264322 title "OMIT: Domain Ontology and Knowledge Acquisition in MicroRNA Target Prediction" @default.
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• W1516264322 doi "https://doi.org/10.1007/978-3-642-16949-6_36" @default.
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