Identification and characterization of the important roles microRNAs (miRNAs) perform in human cancer is an increasingly active research area. Unfortunately, prediction of miRNA target genes remains a challenging task to cancer researchers. Current processes are time-consuming, error-prone, and subject to biologists’ limited prior knowledge. Therefore, we propose a domain-specific knowledge base built upon Ontology for MicroRNA Targets (OMIT) to facilitate knowledge acquisition in miRNA target gene prediction. We describe the ontology design, semantic annotation and data integration, and user-friendly interface and conclude that the OMIT system can assist biologists in unraveling the important roles of miRNAs in human cancer. Thus, it will help clinicians make sound decisions when treating cancer patients.

The authors would like to thank Hardik Shah and Robert Rudnick for helping in software implementation. The authors also appreciate the discussion with Patrick Hayes, Lei He, Wen-chang Lin, Hao Sun, and Xiaowei Wang.

Authors and Affiliations

1. School of Computer and Information Sciences, University of South Alabama, 307 University Blvd. N, Mobile, Alabama, USA

   Jingshan Huang & Christopher Townsend

2. Department of Computer and Information Science, University of Oregon, Eugene, Oregon, USA

   Dejing Dou & Haishan Liu

3. Mitchell Cancer Institute, University of South Alabama, Mobile, Alabama, USA

   Ming Tan

4. Department of Cell Biology and Neuroscience, University of South Alabama, Mobile, Alabama, USA

   Ming Tan

Corresponding author

Correspondence toJingshan Huang.

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