TY - GEN
T1 - OMIT
T2 - 9th Confederated International Conferences on On the Move to Meaningful Internet Systems, OTM 2010: CoopIS 2010, IS 2010, DOA 2010 and ODBASE 2010
AU - Townsend, Christopher
AU - Huang, Jingshan
AU - Dou, Dejing
AU - Dalvi, Shivraj
AU - Hayes, Patrick J.
AU - He, Lei
AU - Lin, Wen Chang
AU - Liu, Haishan
AU - Rudnick, Robert
AU - Shah, Hardik
AU - Sun, Hao
AU - Wang, Xiaowei
AU - Tan, Ming
N1 - Funding Information:
1 School of Computer and Information Sciences University of South Alabama, Mobile, AL 36688, U.S.A. [email protected] http://cis.usouthal.edu/~huang/ 2 Computer and Information Science Department University of Oregon, Eugene, OR 97403, U.S.A. 3 Florida Institute for Human and Machine Cognition Pensacola, FL 32502, U.S.A. 4 College of Science and Technology Armstrong Atlantic State University, Savannah, GA 31419, U.S.A. 5 Institute of Biomedical Sciences Academia Sinica, Taipei, Taiwan 6 Department of Chemical Pathology Chinese University of Hong Kong, Hong Kong, China 7 Department of Radiation Oncology Washington University School of Medicine, St. Louis, MO 63108, U.S.A. 8 Mitchell Cancer Institute University of South Alabama, Mobile, AL 36688, U.S.A. [email protected] http://southalabama.edu/~tan/ 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.
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=78650030503&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-16949-6_36
DO - 10.1007/978-3-642-16949-6_36
M3 - Conference contribution
AN - SCOPUS:78650030503
SN - 3642169481
SN - 9783642169489
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 1160
EP - 1167
BT - On the Move to Meaningful Internet Systems, OTM 2010 - Confederated International Conferences
Y2 - 25 January 2010 through 29 January 2010
ER -