Machine Learning and Atom-Based Quadratic Indices for Proteasome Inhibition Prediction

Machine Learning and Atom-Based Quadratic Indices for Proteasome Inhibition Prediction

The atom-based quadratic indices are used in this work together with some machine learning techniques that includes: support vector machine, artificial neural network, random forest and k-nearest neighbor. This methodology is used for the development of two quantitative structure-activity...

وصف كامل

محفوظ في:
التفاصيل البيبلوغرافية
المؤلف الرئيسي: Le, Thi Thu Huong
التنسيق: مقال
اللغة:English
منشور في: Mol2Net 2016
الموضوعات:
Atom-based quadratic index
Classification and regression model
Machine learning
Proteasome inhibition
QSAR
TOMOCOMD-CARDD software
الوصول للمادة أونلاين:http://repository.vnu.edu.vn/handle/VNU_123/11510
الوسوم: إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
الوصف
الملخص:The atom-based quadratic indices are used in this work together with some machine learning techniques that includes: support vector machine, artificial neural network, random forest and k-nearest neighbor. This methodology is used for the development of two quantitative structure-activity relationship (QSAR) studies for the prediction of proteasome inhibition. A first set consisting of active and non-active classes was predicted with model performances above 85% and 80% in training and validation series, respectively. These results provided new approaches on proteasome inhibitor identification encouraged by virtual screenings procedures

مواد مشابهة