Author: Li, Chun; Zhao, Jialing; Wang, Changzhong; Yao, Yuhua
Title: Protein Sequence Comparison and DNA-binding Protein Identification with Generalized PseAAC and Graphical Representation Document date: 2018_2_23
ID: u1imic5l_53
Snippet: It is important to examine the performance of the newly developed method on an independent dataset. In this experiment, we establish the classifier with the benchmark dataset DNASet and then test it on the independent dataset DNAiSet. To decide the parameter pair (γ, C), we utilize a systematic grid search for and , where integers i and j are in ranges [-3, 3] and [0, 3], respectively. It is find that and are the optimal values for DNASet. With .....
Document: It is important to examine the performance of the newly developed method on an independent dataset. In this experiment, we establish the classifier with the benchmark dataset DNASet and then test it on the independent dataset DNAiSet. To decide the parameter pair (γ, C), we utilize a systematic grid search for and , where integers i and j are in ranges [-3, 3] and [0, 3], respectively. It is find that and are the optimal values for DNASet. With the best pair (γ, C), DNAiSet is fed to the SVM. As a result, our model correctly predicts 68 out of 82 DNA-BPs and 92 out of 100 NBPs. The ACC arrives at 87.91%, with the MCC, sensitivity, specificity, and F1M of 0.756, 82.93%, 92.00% and 86.07%, respectively (see Table 6 ). This demonstrates that our SVM model performs equally well on independent dataset.
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