Paper Info
Title
Toxicity Risks Evaluation Of Unknown Fda Biotransformed Drugs Based On A Multi-Objective Feature Selection Approach
Abstract
The risk factors evaluation of the unknown biotransformed drugs is important in the drug development. However, the experimental methods that are used to perform this task are time-consuming and expensive, therefore, these methods are not suitable to assess a large dataset of drugs at the early stage of the drug development. To avoid these problems, the computational approaches can be used to predict the risk factors of the unknown biotransformed drugs. The dataset used in this study consists of 5909 drugs with 33 chemical descriptors. However, most of these descriptors are irrelevant and this may reduce the prediction accuracy; therefore, the descriptor selection approach is needed. Descriptor (Feature) selection can be considered as a multi-objective optimization problem which has two conflicting objectives, minimizing the number of the selected features and maximizing the dependency degree of the descriptors. In this paper, a new multi-objective approach is developed for the descriptor selection based on the sine-cosine algorithm and the rough set. The proposed approach consists of two stages, the feature selection stage and the predicting of an unknown drug stage. The experimental results proved that the proposed approach achieved high accuracy to all toxic effects and this indicates that it could be used for the prediction of the drug toxicity in the early stage of the drug development. (C) 2019 Elsevier B.V. All rights reserved.
Year
DOI
Venue
2020
10.1016/j.asoc.2019.105509
APPLIED SOFT COMPUTING
Keywords
DocType
Volume
Sine-Cosine Algorithm (SCA), Multi-objective optimization (MOP), Biotransformed drugs, Meta-heuristic
Journal
97
Issue
ISSN
Citations 
Part
1568-4946
1
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Mohamed Abd Elaziz110.34
Yasmine S. Moemen2161.30
Aboul Ella Hassanien31610192.72
Shengwu Xiong418953.59