title: Using random forest algorithm to predict β-hairpin motifs.
abstract: A novel method is presented for predicting β-hairpin motifs in protein sequences. That is Random Forest algorithm on the basis of the multi-characteristic parameters, which include amino acids component of position, hydropathy component of position, predicted secondary structure information and value of auto-correlation function. Firstly, the method is trained and tested on a set of 8,291 β-hairpin motifs and 6,865 non-β-hairpin motifs. The overall accuracy and Matthew's correlation coefficient achieve 82.2% and 0.64 using 5-fold cross-validation, while they achieve 81.7% and 0.63 using the independent test. Secondly, the method is also tested on a set of 4,884 β-hairpin motifs and 4,310 non-β-hairpin motifs which is used in previous studies. The overall accuracy and Matthew's correlation coefficient achieve 80.9% and 0.61 for 5-fold cross-validation, while they achieve 80.6% and 0.60 for the independent test. Compared with the previous, the present result is better. Thirdly, 4,884 β-hairpin motifs and 4,310 non-β-hairpin motifs selected as the training set, and 8,291 β-hairpin motifs and 6,865 non-β-hairpin motifs selected as the independent testing set, the overall accuracy and Matthew's correlation coefficient achieve 81.5% and 0.63 with the independent test.
authors.affiliation: College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051. P.R China.
authors.forename: Shao-Chun
authors.initials: SC
authors.lastname: Jia
authors.forename: Xiu-Zhen
authors.initials: XZ
authors.lastname: Hu
mesh_terms: D000465:Algorithms; D020816:Amino Acid Motifs; D000595:Amino Acid Sequence; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D006801:Humans; D020407:Internet; D008969:Molecular Sequence Data; D011506:Proteins / Q000737:chemistry*
chemical_list: D011506:Proteins