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M. Kumar, M. Bhasin, N. K. Natt and G. P. S. Raghava, “BhairPred: Prediction of β-Hairpins in a Protein from Multiple Alignment Information Using ANN and SVM Techniques,” Nucleic Acids Research, Vol. 33, 2005, pp. 154-159. http://dx.doi.org/10.1093/nar/gki588

has been cited by the following article:

• TITLE: The Comparison between Random Forest and Support Vector Machine Algorithm for Predicting β-Hairpin Motifs in Proteins

  AUTHORS: Shaochun Jia, Xiuzhen Hu, Lixia Sun

  KEYWORDS: Random Forest Algorithm; Support Vector Machine Algorithm; β-Hairpin Motif; Increment of Diversity; Scoring Function; Predicted Secondary Structure Information

  JOURNAL NAME: Engineering, Vol.5 No.10B, December 17, 2013

  ABSTRACT: Based on the research of predictingβ-hairpin motifs in proteins, we apply Random Forest and Support Vector Machine algorithm to predictβ-hairpin motifs in ArchDB40 dataset. The motifs with the loop length of 2 to 8 amino acid residues are extracted as research object and thefixed-length pattern of 12 amino acids are selected. When using the same characteristic parameters and the same test method, Random Forest algorithm is more effective than Support Vector Machine. In addition, because of Random Forest algorithm doesn’t produce overfitting phenomenon while the dimension of characteristic parameters is higher, we use Random Forest based on higher dimension characteristic parameters to predictβ-hairpin motifs. The better prediction results are obtained; the overall accuracy and Matthew’s correlation coefficient of 5-fold cross-validation achieve 83.3% and 0.59, respectively.