Predicting Presynaptic and Postsynaptic Neurotoxins by Developing Feature Selection Technique

Predicting Presynaptic and Postsynaptic Neurotoxins by Developing Feature Selection Technique

Presynaptic and postsynaptic neurotoxins are proteins which act at the presynaptic and postsynaptic membrane. Correctly predicting presynaptic and postsynaptic neurotoxins will provide important clues for drug-target discovery and drug design. In this study, we developed a theoretical method to disc...

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Main Authors: Hua Tang, Yunchun Yang, Chunmei Zhang, Rong Chen, Po Huang, Chenggang Duan, Ping Zou
Format: Article
Language:English
Published: Hindawi Limited 2017-01-01
Series:BioMed Research International
Online Access:http://dx.doi.org/10.1155/2017/3267325
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spelling doaj-969152ef3e144bb5a8ad62edf5df502c2020-11-24T22:51:19ZengHindawi LimitedBioMed Research International2314-61332314-61412017-01-01201710.1155/2017/32673253267325Predicting Presynaptic and Postsynaptic Neurotoxins by Developing Feature Selection TechniqueHua Tang0Yunchun Yang1Chunmei Zhang2Rong Chen3Po Huang4Chenggang Duan5Ping Zou6Department of Pathophysiology, Southwest Medical University, Luzhou 646000, ChinaDepartment of Anesthesiology, The Affiliated Traditional Chinese Medical Hospital of Southwest Medical University, Luzhou 646000, ChinaDepartment of Pathophysiology, Southwest Medical University, Luzhou 646000, ChinaDepartment of Pathophysiology, Southwest Medical University, Luzhou 646000, ChinaDepartment of Pathophysiology, Southwest Medical University, Luzhou 646000, ChinaDepartment of Pathophysiology, Southwest Medical University, Luzhou 646000, ChinaDepartment of Pathophysiology, Southwest Medical University, Luzhou 646000, ChinaPresynaptic and postsynaptic neurotoxins are proteins which act at the presynaptic and postsynaptic membrane. Correctly predicting presynaptic and postsynaptic neurotoxins will provide important clues for drug-target discovery and drug design. In this study, we developed a theoretical method to discriminate presynaptic neurotoxins from postsynaptic neurotoxins. A strict and objective benchmark dataset was constructed to train and test our proposed model. The dipeptide composition was used to formulate neurotoxin samples. The analysis of variance (ANOVA) was proposed to find out the optimal feature set which can produce the maximum accuracy. In the jackknife cross-validation test, the overall accuracy of 94.9% was achieved. We believe that the proposed model will provide important information to study neurotoxins.http://dx.doi.org/10.1155/2017/3267325
collection DOAJ
language English
format Article
sources DOAJ
author Hua Tang
Yunchun Yang
Chunmei Zhang
Rong Chen
Po Huang
Chenggang Duan
Ping Zou
spellingShingle Hua Tang
Yunchun Yang
Chunmei Zhang
Rong Chen
Po Huang
Chenggang Duan
Ping Zou
Predicting Presynaptic and Postsynaptic Neurotoxins by Developing Feature Selection Technique
BioMed Research International
author_facet Hua Tang
Yunchun Yang
Chunmei Zhang
Rong Chen
Po Huang
Chenggang Duan
Ping Zou
author_sort Hua Tang
title Predicting Presynaptic and Postsynaptic Neurotoxins by Developing Feature Selection Technique
title_short Predicting Presynaptic and Postsynaptic Neurotoxins by Developing Feature Selection Technique
title_full Predicting Presynaptic and Postsynaptic Neurotoxins by Developing Feature Selection Technique
title_fullStr Predicting Presynaptic and Postsynaptic Neurotoxins by Developing Feature Selection Technique
title_full_unstemmed Predicting Presynaptic and Postsynaptic Neurotoxins by Developing Feature Selection Technique
title_sort predicting presynaptic and postsynaptic neurotoxins by developing feature selection technique
publisher Hindawi Limited
series BioMed Research International
issn 2314-6133
2314-6141
publishDate 2017-01-01
description Presynaptic and postsynaptic neurotoxins are proteins which act at the presynaptic and postsynaptic membrane. Correctly predicting presynaptic and postsynaptic neurotoxins will provide important clues for drug-target discovery and drug design. In this study, we developed a theoretical method to discriminate presynaptic neurotoxins from postsynaptic neurotoxins. A strict and objective benchmark dataset was constructed to train and test our proposed model. The dipeptide composition was used to formulate neurotoxin samples. The analysis of variance (ANOVA) was proposed to find out the optimal feature set which can produce the maximum accuracy. In the jackknife cross-validation test, the overall accuracy of 94.9% was achieved. We believe that the proposed model will provide important information to study neurotoxins.
url http://dx.doi.org/10.1155/2017/3267325
work_keys_str_mv AT huatang predictingpresynapticandpostsynapticneurotoxinsbydevelopingfeatureselectiontechnique
AT yunchunyang predictingpresynapticandpostsynapticneurotoxinsbydevelopingfeatureselectiontechnique
AT chunmeizhang predictingpresynapticandpostsynapticneurotoxinsbydevelopingfeatureselectiontechnique
AT rongchen predictingpresynapticandpostsynapticneurotoxinsbydevelopingfeatureselectiontechnique
AT pohuang predictingpresynapticandpostsynapticneurotoxinsbydevelopingfeatureselectiontechnique
AT chenggangduan predictingpresynapticandpostsynapticneurotoxinsbydevelopingfeatureselectiontechnique
AT pingzou predictingpresynapticandpostsynapticneurotoxinsbydevelopingfeatureselectiontechnique
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