Abstract
Much benefit to biology research and drug design, prediction of neurotoxin graduallybecame 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 predictpresynaptic 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. Key words: Prediction, bi-layer support vector machine, pseudo amino acid composition, approximate entropy, dipeptide.
Highlights
There are nearly 3000 species of spiders and 2340 species of snakes living in the world, and hundreds of them are poisonous
The information about neurotoxins can be obtained by experimental technology, but computer aided prediction is less time consuming and costly, so computer aided prediction of presynaptic and postsynaptic neurotoxins would be very helpful in obtaining these information
Support vector machine (SVM) is an effective tool for classification and prediction, which has been used in various fields related to protein function prediction (Huang and Shi, 2005; Zhang et al, 2006; Zhou et al, 2008; Shi et al, 2008; Lin et al, 2009), but methods only using a single classifier have some limitations in the prediction (Chou and Shen, 2006a, c)
Summary
There are nearly 3000 species of spiders and 2340 species of snakes living in the world, and hundreds of them are poisonous. There are a lot of studies in these fields, as an effective feature extraction method, Pseudo amino acid compositions (PseAA) are usually used to represent a protein sequence with a discrete model yet without completely losing its sequence-order information (Chou, 2001). Support vector machine (SVM) is an effective tool for classification and prediction, which has been used in various fields related to protein function prediction (Huang and Shi, 2005; Zhang et al, 2006; Zhou et al, 2008; Shi et al, 2008; Lin et al, 2009), but methods only using a single classifier have some limitations in the prediction (Chou and Shen, 2006a, c). A multi-classifier named “bi-layer SVM” was built to further improve the prediction accuracy of presynaptic and postsynaptic neurotoxins, and a relatively good predictive result was obtained
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