12 research outputs found
Additional file 1: Table S1. of SkipCPP-Pred: an improved and promising sequence-based predictor for predicting cell-penetrating peptides
Feature ranking of the proposed adaptive k-skip-2-g features. IG(x,c)a denotes information gain score. Higher IG(x,c) for a feature means the feature is more discriminative. Table S2. Performance of the Random Forest classifier with different tree numbers on the benchmark dataset CPP924 with the jackknife validation test. Note that the tree number is changed from 10 to 500 with the incremental step of 10. (DOCX 61Â kb
The accuracy of different m values on PDB1075 (Five-fold cross validation).
<p>The accuracy of different m values on PDB1075 (Five-fold cross validation).</p
The AUROC comparison of seven feature combinations through Jackknife cross-validation on PDB1075 dataset.
<p>The AUROC comparison of seven feature combinations through Jackknife cross-validation on PDB1075 dataset.</p
The computational time of feature extraction and jackknife test evaluation on PDB1075.
<p>The computational time of feature extraction and jackknife test evaluation on PDB1075.</p
The accuracy of different lg values on PDB1075 (Five-fold cross validation).
<p>The accuracy of different lg values on PDB1075 (Five-fold cross validation).</p
The performance of different features on PDB1075 dataset (Jackknife test evaluation).
<p>The performance of different features on PDB1075 dataset (Jackknife test evaluation).</p
The performance of our method and other existing methods on PDB186 dataset.
<p>The performance of our method and other existing methods on PDB186 dataset.</p
Original values of six physicochemical properties of 20 amino acid types.
<p>Original values of six physicochemical properties of 20 amino acid types.</p
The feature score through SVM-RFE+CBR on the dataset of PDB1075.
<p>The x-axis represents the feature index.</p