Predicting protein subcellular localization based on information content of gene ontology terms.
Predicting the location where a protein resides within a cell is important in cell biology. Computational approaches to this issue have attracted more and more attentions from the community of biomedicine. Among the protein features used to predict the subcellular localization of proteins, the feature derived from Gene Ontology (GO) has been shown to be superior to others. However, most of the sights in this field are set on the presence or absence of some predefined GO terms. We proposed a method to derive information from the intrinsic structure of the GO graph. The feature vector was constructed with each element in it representing the information content of the GO term annotating to a protein investigated, and the support vector machines was used as classifier to test our extracted features. Evaluation experiments were conducted on three protein datasets and the results show that our method can enhance eukaryotic and human subcellular location prediction accuracy by up to 1.1% better than previous studies that also used GO-based features. Especially in the scenario where the cellular component annotation is absent, our method can achieved satisfied results with an overall accuracy of more than 87%.
Department of Computer Science, Guangzhou Maritime Institute, Room 803 Building 88, Dashabei Road, Huangpu District, 510275, Guangzhou, PR China. Electronic address: 845996912@qq.com.
Shu-Bo
SB
Zhang
Department of Shipping, Guangzhou Marine Institute, Room 205 Shipping Building, Hongshan NO.3 Road, Huangpu District, 510275, Guangzhou, PR China. Electronic address: tangqiangrong@hotmail.com.
Qiang-Rong
QR
Tang
D030562:Databases, Protein; D063990:Gene Ontology; D011506:Proteins / Q000378:metabolism*; D013347:Subcellular Fractions / Q000378:metabolism*; D060388:Support Vector Machine
D011506:Proteins
Gene ontology; Information content; Protein subcellular localization; Support vector machines