title: Identification of apolipoprotein using feature selection technique.
abstract: Apolipoprotein is a kind of protein which can transport the lipids through the lymphatic and circulatory systems. The abnormal expression level of apolipoprotein always causes angiocardiopathy. Thus, correct recognition of apolipoprotein from proteomic data is very crucial to the comprehension of cardiovascular system and drug design. This study is to develop a computational model to predict apolipoproteins. In the model, the apolipoproteins and non-apolipoproteins were collected to form benchmark dataset. On the basis of the dataset, we extracted the g-gap dipeptide composition information from residue sequences to formulate protein samples. To exclude redundant information or noise, the analysis of various (ANOVA)-based feature selection technique was proposed to find out the best feature subset. The support vector machine (SVM) was selected as discrimination algorithm. Results show that 96.2% of sensitivity and 99.3% of specificity were achieved in five-fold cross-validation. These findings open new perspectives to improve apolipoproteins prediction by considering the specific dipeptides. We expect that these findings will help to improve drug development in anti-angiocardiopathy disease.
authors.affiliation: Department of Pathophysiology, Southwest Medical University, Luzhou 646000, China.
authors.forename: Hua
authors.initials: H
authors.lastname: Tang
authors.affiliation: Department of Pathophysiology, Southwest Medical University, Luzhou 646000, China.
authors.forename: Ping
authors.initials: P
authors.lastname: Zou
authors.affiliation: Department of Pathophysiology, Southwest Medical University, Luzhou 646000, China.
authors.forename: Chunmei
authors.initials: C
authors.lastname: Zhang
authors.affiliation: Department of Pathophysiology, Southwest Medical University, Luzhou 646000, China.
authors.forename: Rong
authors.initials: R
authors.lastname: Chen
authors.affiliation: Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.|Department of Physics, School of Sciences, Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan 063009 China.
authors.forename: Wei
authors.initials: W
authors.lastname: Chen
authors.affiliation: Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.
authors.forename: Hao
authors.initials: H
authors.lastname: Lin
mesh_terms: D000465:Algorithms; D001053:Apolipoproteins / Q000032:analysis*; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D006801:Humans; D020407:Internet
chemical_list: D001053:Apolipoproteins