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SDTRLS: Predicting Drug-Target Interactions for Complex Diseases Based on Chemical Substructures
المؤلفون المشاركون
Yan, Cheng
Lan, Wei
Pan, Yi
Wang, Jianxin
Wu, Fang-Xiang
المصدر
العدد
المجلد 2017، العدد 2017 (31 ديسمبر/كانون الأول 2017)، ص ص. 1-10، 10ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2017-12-03
دولة النشر
مصر
عدد الصفحات
10
التخصصات الرئيسية
الملخص EN
It is well known that drug discovery for complex diseases via biological experiments is a time-consuming and expensive process.
Alternatively, the computational methods provide a low-cost and high-efficiency way for predicting drug-target interactions (DTIs) from biomolecular networks.
However, the current computational methods mainly deal with DTI predictions of known drugs; there are few methods for large-scale prediction of failed drugs and new chemical entities that are currently stored in some biological databases may be effective for other diseases compared with their originally targeted diseases.
In this study, we propose a method (called SDTRLS) which predicts DTIs through RLS-Kron model with chemical substructure similarity fusion and Gaussian Interaction Profile (GIP) kernels.
SDTRLS can be an effective predictor for targets of old drugs, failed drugs, and new chemical entities from large-scale biomolecular network databases.
Our computational experiments show that SDTRLS outperforms the state-of-the-art SDTNBI method; specifically, in the G protein-coupled receptors (GPCRs) external validation, the maximum and the average AUC values of SDTRLS are 0.842 and 0.826, respectively, which are superior to those of SDTNBI, which are 0.797 and 0.766, respectively.
This study provides an important basis for new drug development and drug repositioning based on biomolecular networks.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Yan, Cheng& Wang, Jianxin& Lan, Wei& Wu, Fang-Xiang& Pan, Yi. 2017. SDTRLS: Predicting Drug-Target Interactions for Complex Diseases Based on Chemical Substructures. Complexity،Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1142665
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Yan, Cheng…[et al.]. SDTRLS: Predicting Drug-Target Interactions for Complex Diseases Based on Chemical Substructures. Complexity No. 2017 (2017), pp.1-10.
https://search.emarefa.net/detail/BIM-1142665
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Yan, Cheng& Wang, Jianxin& Lan, Wei& Wu, Fang-Xiang& Pan, Yi. SDTRLS: Predicting Drug-Target Interactions for Complex Diseases Based on Chemical Substructures. Complexity. 2017. Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1142665
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1142665
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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