计算机科学
人工智能
深度学习
人工神经网络
卷积神经网络
机器学习
药品
模式识别(心理学)
支持向量机
深层神经网络
循环神经网络
学习迁移
作者
Chengcheng Zhang,Tianyi Zang
出处
期刊:Bioinformatics and Biomedicine
日期:2020-12-16
卷期号:: 1708-1713
标识
DOI:10.1109/bibm49941.2020.9313404
摘要
Predicting drug-drug interactions (DDIs) is one of the major concerns in patients’ medication, which is crucial for patient safety and public health. Most of studies study whether drugs interact or not. In this study, we focus on 65 categories of drug-drug interaction-associated events and proposed a new method based on convolutional neural network (CNN), named CNN-DDI, for predicting DDIs. First, the categories, targets, pathways and enzymes of drugs were extracted as the features of drugs, which constructed the input of CNN-DDI. Then, these features were as input vectors of our CNN network, and the output is the prediction of drug-drug interaction-associated events’ categories. In the computational experiments, the CNN-DDI method achieves an accuracy rate up to 0.8914, an area under the precision-recall curve up to 0.9322. And the experiments also prove using feature combinations outperforms one feature. Compared with other state-of-the-art methods, the CNN-DDI method has better performance in the superiority and the effectiveness for predicting DDI’s events.
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