OMIT: domain ontology and knowledge acquisition in microRNA target prediction

  • Authors:

  • Christopher Townsend;Jingshan Huang;Dejing Dou;Shivraj Dalvi;Patrick J. Hayes;Lei He;Wen-Chang Lin;Haishan Liu;Robert Rudnick;Hardik Shah;Hao Sun;Xiaowei Wang;Ming Tan

  • Affiliations:

  • School of Computer and Information Sciences, University of South Alabama, Mobile, AL;School of Computer and Information Sciences, University of South Alabama, Mobile, AL;Computer and Information Science Department, University of Oregon, Eugene, OR;School of Computer and Information Sciences, University of South Alabama, Mobile, AL;Florida Institute for Human and Machine Cognition, Pensacola, FL;College of Science and Technology, Armstrong Atlantic State University, Savannah, GA;Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan;Computer and Information Science Department, University of Oregon, Eugene, OR;School of Computer and Information Sciences, University of South Alabama, Mobile, AL;School of Computer and Information Sciences, University of South Alabama, Mobile, AL;Department of Chemical Pathology, Chinese University of Hong Kong, Hong Kong, China;Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO;Mitchell Cancer Institute, University of South Alabama, Mobile, AL

  • Venue:
  • OTM'10 Proceedings of the 2010 international conference on On the move to meaningful internet systems: Part II
  • Year:
  • 2010

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Abstract

The 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.