Predicting Presynaptic and Postsynaptic Neurotoxins by Developing Feature Selection Technique
Joint Authors
Tang, Hua
Yang, Yunchun
Zhang, Chunmei
Chen, Rong
Huang, Po
Duan, Chenggang
Zou, Ping
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-4, 4 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-02-12
Country of Publication
Egypt
No. of Pages
4
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Tang, Hua& Yang, Yunchun& Zhang, Chunmei& Chen, Rong& Huang, Po& Duan, Chenggang…[et al.]. 2017. Predicting Presynaptic and Postsynaptic Neurotoxins by Developing Feature Selection Technique. BioMed Research International،Vol. 2017, no. 2017, pp.1-4.
https://search.emarefa.net/detail/BIM-1135895
Modern Language Association (MLA)
Tang, Hua…[et al.]. Predicting Presynaptic and Postsynaptic Neurotoxins by Developing Feature Selection Technique. BioMed Research International No. 2017 (2017), pp.1-4.
https://search.emarefa.net/detail/BIM-1135895
American Medical Association (AMA)
Tang, Hua& Yang, Yunchun& Zhang, Chunmei& Chen, Rong& Huang, Po& Duan, Chenggang…[et al.]. Predicting Presynaptic and Postsynaptic Neurotoxins by Developing Feature Selection Technique. BioMed Research International. 2017. Vol. 2017, no. 2017, pp.1-4.
https://search.emarefa.net/detail/BIM-1135895
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1135895