| Issue |
MATEC Web Conf.
Volume 173, 2018
2018 International Conference on Smart Materials, Intelligent Manufacturing and Automation (SMIMA 2018)
|
|
|---|---|---|
| Article Number | 02007 | |
| Number of page(s) | 5 | |
| Section | Automation and Nontraditional Manufacturing | |
| DOI | https://doi.org/10.1051/matecconf/201817302007 | |
| Published online | 19 June 2018 | |
Improved Label Propagation Model to Predict Drug - drug Interactions
Advanced Design and Intelligent Computing Laboratory, Dalian University, Dalian, China
* Corresponding author: zhangq26@126.com
Drug-drug interactions (DDIs) is one of the most concerned issues in drug design. Accurate prediction of potential DDIs in clinical trials can reduce the occurrence of side effects in real life of drugs. Therefore, we propose a model to predict DDIs. The model integrates several methods that can improve label propagation algorithm. Firstly, the chi-square test (CHI) method is adopted to filter or select the features that contain a large amount of information. Secondly, the sample similarity calculation method is reconstructed by label similarity and feature similarity. Then the label initialization information of unlabeled samples is constructed. Finally, we use label propagation algorithm to estimate the labels of the unlabeled drugs. The results show that the proposed model can obtain higher the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPR), which provides a favorable guarantee for the discovery of DDIs in the clinical stage.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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