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.
Highlights
When Support Vector Machine (SVM) is used as prediction algorithm, Acc and MCC are only 79.4% and 0.49, respectively
The results show that RF algorithm is better than SVM
When SVM algorithm is used as prediction algorithm, Acc and MCC are only 77.0% and 0.43, respectively
Summary
When using the same characteristic parameters and the same test method, Random Forest algorithm is more effective than Support Vector Machine. 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. In 2008, our group’s Hu [7] et al predicted β-hairpins in ArchDB40 dataset by using SVM, the overall accuracy and Matthew’s correlation coefficients are 79.9% and 0.59, respectively.
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