A Genetic Algorithm Based Support Vector Machine Model for Blood-Brain Barrier Penetration Prediction
المؤلفون المشاركون
Zhang, Daqing
Zheng, Mingyue
Xiao, Jianfeng
Zhou, Nannan
Luo, Xiaomin
Jiang, Hualiang
Chen, Kaixian
المصدر
العدد
المجلد 2015، العدد 2015 (31 ديسمبر/كانون الأول 2015)، ص ص. 1-13، 13ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2015-10-04
دولة النشر
مصر
عدد الصفحات
13
التخصصات الرئيسية
الملخص 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.
نمط استشهاد جمعية علماء النفس الأمريكية (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
نمط استشهاد الجمعية الأمريكية للغات الحديثة (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
نمط استشهاد الجمعية الطبية الأمريكية (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
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1054927
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر