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...
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oai:112.137.131.14:VNU_123-115102017-04-05T14:27:40Z Machine Learning and Atom-Based Quadratic Indices for Proteasome Inhibition Prediction Le, Thi Thu Huong Atom-based quadratic index Classification and regression model Machine learning Proteasome inhibition QSAR TOMOCOMD-CARDD software 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 2016-05-30T17:55:28Z 2016-05-30T17:55:28Z 2015 Article 1422-0067 http://repository.vnu.edu.vn/handle/VNU_123/11510 en application/pdf Mol2Net |
| institution |
Vietnam National University, Hanoi |
| building |
VNU Library & Information Center |
| country |
Vietnam |
| collection |
VNU Digital Repository |
| language |
English |
| topic |
Atom-based quadratic index Classification and regression model Machine learning Proteasome inhibition QSAR TOMOCOMD-CARDD software |
| spellingShingle |
Atom-based quadratic index Classification and regression model Machine learning Proteasome inhibition QSAR TOMOCOMD-CARDD software Le, Thi Thu Huong Machine Learning and Atom-Based Quadratic Indices for Proteasome Inhibition Prediction |
| description |
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 |
| format |
Article |
| author |
Le, Thi Thu Huong |
| author_facet |
Le, Thi Thu Huong |
| author_sort |
Le, Thi Thu Huong |
| title |
Machine Learning and Atom-Based Quadratic Indices for Proteasome Inhibition Prediction |
| title_short |
Machine Learning and Atom-Based Quadratic Indices for Proteasome Inhibition Prediction |
| title_full |
Machine Learning and Atom-Based Quadratic Indices for Proteasome Inhibition Prediction |
| title_fullStr |
Machine Learning and Atom-Based Quadratic Indices for Proteasome Inhibition Prediction |
| title_full_unstemmed |
Machine Learning and Atom-Based Quadratic Indices for Proteasome Inhibition Prediction |
| title_sort |
machine learning and atom-based quadratic indices for proteasome inhibition prediction |
| publisher |
Mol2Net |
| publishDate |
2016 |
| url |
http://repository.vnu.edu.vn/handle/VNU_123/11510 |
| _version_ |
1680966044458418176 |