![](/images/graphics-bg.png)
A Genetic Algorithm Based Support Vector Machine Model for Blood-Brain Barrier Penetration Prediction
Joint Authors
Zhang, Daqing
Zheng, Mingyue
Xiao, Jianfeng
Zhou, Nannan
Luo, Xiaomin
Jiang, Hualiang
Chen, Kaixian
Source
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-10-04
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
Blood-brain barrier (BBB) is a highly complex physical barrier determining what substances are allowed to enter the brain.
Support vector machine (SVM) is a kernel-based machine learning method that is widely used in QSAR study.
For a successful SVM model, the kernel parameters for SVM and feature subset selection are the most important factors affecting prediction accuracy.
In most studies, they are treated as two independent problems, but it has been proven that they could affect each other.
We designed and implemented genetic algorithm (GA) to optimize kernel parameters and feature subset selection for SVM regression and applied it to the BBB penetration prediction.
The results show that our GA/SVM model is more accurate than other currently available log BB models.
Therefore, to optimize both SVM parameters and feature subset simultaneously with genetic algorithm is a better approach than other methods that treat the two problems separately.
Analysis of our log BB model suggests that carboxylic acid group, polar surface area (PSA)/hydrogen-bonding ability, lipophilicity, and molecular charge play important role in BBB penetration.
Among those properties relevant to BBB penetration, lipophilicity could enhance the BBB penetration while all the others are negatively correlated with BBB penetration.
American Psychological Association (APA)
Zhang, Daqing& Xiao, Jianfeng& Zhou, Nannan& Zheng, Mingyue& Luo, Xiaomin& Jiang, Hualiang…[et al.]. 2015. A Genetic Algorithm Based Support Vector Machine Model for Blood-Brain Barrier Penetration Prediction. BioMed Research International،Vol. 2015, no. 2015, pp.1-13.
https://search.emarefa.net/detail/BIM-1054927
Modern Language Association (MLA)
Zhang, Daqing…[et al.]. A Genetic Algorithm Based Support Vector Machine Model for Blood-Brain Barrier Penetration Prediction. BioMed Research International No. 2015 (2015), pp.1-13.
https://search.emarefa.net/detail/BIM-1054927
American Medical Association (AMA)
Zhang, Daqing& Xiao, Jianfeng& Zhou, Nannan& Zheng, Mingyue& Luo, Xiaomin& Jiang, Hualiang…[et al.]. A Genetic Algorithm Based Support Vector Machine Model for Blood-Brain Barrier Penetration Prediction. BioMed Research International. 2015. Vol. 2015, no. 2015, pp.1-13.
https://search.emarefa.net/detail/BIM-1054927
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1054927