title: Two multi-classification strategies used on SVM to predict protein structural classes by using auto covariance.
abstract: Machine learning methods play the very important role in protein secondary structure prediction and other related works. On condition of a certain approach, the prediction qualities mostly depend on the ways of representing protein sequences into numeric features. In this paper, two Support Vector Machine (SVM) multi-classification strategies, "one-against-one" (1-a-1) and "one-against-all" (1-a-a), were used in protein structural classes identification. Auto covariance (AC), which transforms the physicochemical properties of the amino acids of the proteins into a data matrix, focuses on the neighboring effects and the interactions between residues in protein sequences. "1-a-1" approach was used on SVM to predict protein structural classes and obtained very promising overall accuracy 90.69% by Jackknife test. It was more than 10% higher than the accuracy obtained by using "1-a-a". Experimental results led to the finding that the SVM predictor constructed by "1-a-1" can avoid the appearance of biased prediction accuracy. This current method, using the protein primary sequence information described by auto covariance (AC) and "1-a-1" approach on SVM, should play an important complementary role in other related applications.
authors.affiliation: College of Chemistry, Sichuan University, Chengdu, China.
authors.forename: Jiang
authors.initials: J
authors.lastname: Wu
authors.forename: Yi-Zhou
authors.initials: YZ
authors.lastname: Li
authors.forename: Meng-Long
authors.initials: ML
authors.lastname: Li
authors.forename: Le-Zheng
authors.initials: LZ
authors.lastname: Yu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D005822:Genetic Vectors; D010363:Pattern Recognition, Automated / Q000379:methods; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry* / Q000145:classification*; D015203:Reproducibility of Results; D020539:Sequence Analysis, Protein / Q000379:methods; D012984:Software
chemical_list: D011506:Proteins