Title :
Identifying gene-disease associations using word proximity and similarity of Gene Ontology terms
Author :
Hou, Wen-Juan ; Chen, Li-Che ; Lu, Chieh-Shiang
Author_Institution :
Dept. of Comput. Sci. & Inf. Eng., Nat. Taiwan Normal Univ., Taipei, Taiwan
Abstract :
Associating genes with diseases is an active area of research because it is useful for helping human health with applications to clinical diagnosis and therapy. This paper proposes two methods to guide the associations between genes and diseases: (1) making use of the proximity relationship between genes and diseases and (2) utilizing GO terms shared by genes and diseases for similarity comparison. The experiments show that associations utilizing GO terms perform better than using word proximity. The results reveal that the GO terms act as a good gene-disease association feature.
Keywords :
cellular biophysics; diseases; genetics; medical diagnostic computing; ontologies (artificial intelligence); patient diagnosis; patient treatment; clinical diagnosis; gene ontology terms; gene-disease associations; human health; similarity; therapy; word proximity; Bioinformatics; Databases; Diseases; Humans; Ontologies; Proteins; Tagging; Gene Ontology; bioinformatics; gene-disease association; text mining; word proximity relationship;
Conference_Titel :
Biomedical Engineering and Informatics (BMEI), 2011 4th International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-9351-7
DOI :
10.1109/BMEI.2011.6098702