Prediction of Drug-Drug Interactions Based on Multi-layer Feature Selection and Data Balance

YUE Kejuan; ZOU Beiji; WANG Lei; LI Xiao; ZENG Min; WEI Faran

doi:10.1049/cje.2017.04.005

Volume 26 Issue 3

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YUE Kejuan, ZOU Beiji, WANG Lei, et al., “Prediction of Drug-Drug Interactions Based on Multi-layer Feature Selection and Data Balance,” Chinese Journal of Electronics, vol. 26, no. 3, pp. 585-590, 2017, doi: 10.1049/cje.2017.04.005

Citation:

YUE Kejuan, ZOU Beiji, WANG Lei, et al., “Prediction of Drug-Drug Interactions Based on Multi-layer Feature Selection and Data Balance,” Chinese Journal of Electronics, vol. 26, no. 3, pp. 585-590, 2017, doi: 10.1049/cje.2017.04.005

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Prediction of Drug-Drug Interactions Based on Multi-layer Feature Selection and Data Balance

doi: 10.1049/cje.2017.04.005

YUE Kejuan^1,2,3,
ZOU Beiji^{1,2
,},
WANG Lei^1,2,
LI Xiao^1,2,
ZENG Min^1,2,
WEI Faran^1,2

1.
School of Information Science and Engineering, Central South University, Changsha 410083, China;
2.
Center for Ophthalmic Imaging Research, Central South university, Changsha 410083, China;
3.
School of Information Science and Engineering, Hunan First Normal University, Changsha 410205, China

Funds: This work is supported by the National Natural Science Foundation of China (No.61573380, No.61672542), and the Scientific Research Fund of Hunan Provincial Education Department (No.13C143).

More Information

Corresponding author: ZOU Beiji (corresponding author) received the B.S., M.S., and Ph.D degrees from Zhejiang University in 1982, Qinghua University in 1984 and Hunan University in 2001 respectively. He is currently a Professor and served as the dean at the school of Information Science and Engineering at Central South University. His research interests include computer graphics and image processing. (Email:bjzou@csu.edu.cn)
Received Date: 2016-07-08
Rev Recd Date: 2016-10-17
Publish Date: 2017-05-10

Abstract

Abstract

Drug-drug interactions (DDIs) occur when two drugs react with each other, which may cause unexpected side effects and even death of the patient. Methods that use adverse event reports to predict unexpected DDIs are limited by two critical yet challenging issues. One is the difficulty of selecting discriminative features from numerous redundant and irrelevant adverse events for modeling. The other is the data imbalance, i.e., the drug pairs causing adverse effects are far less than those not causing adverse effects, which leads to poor accuracy of DDIs detection. We propose a multi-layer feature selection method to select discriminative adverse events and apply an over-sampling technique to make the data balanced. The experimental results show that the validation accuracy of positive DDIs on the Canada Vigilance Adverse Reaction Online Database increases to two times, and 110 DDIs are identified on the drug interactions checker of Drugs.com in USA.
- Adverse event reports,
- Drug-drug interactions (DDIs),
- Feature selection,
- Data balance

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References(27)

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