A Genetic Algorithm Based Support Vector Machine Model for Blood-Brain Barrier Penetration Prediction.
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 s...
| Publicado en: | BioMed Research International Vol. 2015; pp. 1 - 14 |
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| Autores principales: | , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Wiley-Blackwell
10/4/2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=110447167&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 110447167 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 10/4/2015 vid: 2015 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 110447167 110447167 NLM26504797 10.1155/2015/292683 NLM26504797 PMC4609370 110447167 ppf: 1 ppct: 13 formats: fmt: @attributes: type: P tig: atl: A Genetic Algorithm Based Support Vector Machine Model for Blood-Brain Barrier Penetration Prediction. aug: au: Zhang, Daqing Xiao, Jianfeng Zhou, Nannan Zheng, Mingyue Luo, Xiaomin Jiang, Hualiang Chen, Kaixian affil: Center for Systems Biology, Soochow University, Suzhou 215006, China sug: ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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