Prediction of presynaptic and postsynaptic neurotoxins by bi-layer support vector machine with multi-features

Abstract

Much benefit to biology research and drug design, prediction of neurotoxin gradually became a necessary and popular task in recent year. In this paper, based on multi-feature extraction strategies from primary sequences and support vector machine, a novel Multi-classifier system named bi-layer support vector machine was proposed to predict presynaptic and postsynaptic neurotoxins, and obtained satisfactory results with 98.5% prediction accuracies for presynaptic neurotoxins and 99.18% for postsynaptic neurotoxins, the Matthew's correlation coefficient was 0.9767. The satisfactory results showed that, the current method might play a complementary role to other existing methods for predicting presynaptic and postsynaptic neurotoxins.

Predicting Presynaptic and Postsynaptic Neurotoxins by Developing Feature Selection Technique

“…Thus, our method is superior to Yang's method. Song developed bilayer support vector machine to improve the accuracy [7]. We noticed that the sequence identity of the benchmark dataset reaches 90% which results in the overestimation of the method.…”

“…SVM is a very popular machine learning method and has been widely used in bioinformatics [7, 14–18]. The basic idea of SVM is to transform the input vector into a high-dimension Hilbert space and to determine a separating hyperplane in this space.…”

“…The overall accuracy was 90.39% in jackknife cross-validation, which is far from satisfactory. Subsequently, Song proposed using bilayer support vector machine (SVM) to improve prediction accuracy based on a new benchmark dataset [7]. Although the overall accuracy was dramatically improved, the sequence identity of the dataset was so high that the results were overestimated.…”