A Novel Triple Matrix Factorization Method for Detecting Drug-Side Effect Association Based on Kernel Target Alignment
المؤلفون المشاركون
Tang, J.
Guo, Fei
Guo, Xiaoyi
Zhou, Wei
Yu, Yan
Ding, Yijie
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-11، 11ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-05-29
دولة النشر
مصر
عدد الصفحات
11
التخصصات الرئيسية
الملخص EN
All drugs usually have side effects, which endanger the health of patients.
To identify potential side effects of drugs, biological and pharmacological experiments are done but are expensive and time-consuming.
So, computation-based methods have been developed to accurately and quickly predict side effects.
To predict potential associations between drugs and side effects, we propose a novel method called the Triple Matrix Factorization- (TMF-) based model.
TMF is built by the biprojection matrix and latent feature of kernels, which is based on Low Rank Approximation (LRA).
LRA could construct a lower rank matrix to approximate the original matrix, which not only retains the characteristics of the original matrix but also reduces the storage space and computational complexity of the data.
To fuse multivariate information, multiple kernel matrices are constructed and integrated via Kernel Target Alignment-based Multiple Kernel Learning (KTA-MKL) in drug and side effect space, respectively.
Compared with other methods, our model achieves better performance on three benchmark datasets.
The values of the Area Under the Precision-Recall curve (AUPR) are 0.677, 0.685, and 0.680 on three datasets, respectively.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Guo, Xiaoyi& Zhou, Wei& Yu, Yan& Ding, Yijie& Tang, J.& Guo, Fei. 2020. A Novel Triple Matrix Factorization Method for Detecting Drug-Side Effect Association Based on Kernel Target Alignment. BioMed Research International،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1134104
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Guo, Xiaoyi…[et al.]. A Novel Triple Matrix Factorization Method for Detecting Drug-Side Effect Association Based on Kernel Target Alignment. BioMed Research International No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1134104
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Guo, Xiaoyi& Zhou, Wei& Yu, Yan& Ding, Yijie& Tang, J.& Guo, Fei. A Novel Triple Matrix Factorization Method for Detecting Drug-Side Effect Association Based on Kernel Target Alignment. BioMed Research International. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1134104
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1134104
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