CNN-Siam: multimodal siamese CNN-based deep learning approach for drug‒drug interaction prediction.

Journal: BMC bioinformatics
Published Date:

Abstract

BACKGROUND: Drug‒drug interactions (DDIs) are reactions between two or more drugs, i.e., possible situations that occur when two or more drugs are used simultaneously. DDIs act as an important link in both drug development and clinical treatment. Since it is not possible to study the interactions of such a large number of drugs using experimental means, a computer-based deep learning solution is always worth investigating. We propose a deep learning-based model that uses twin convolutional neural networks to learn representations from multimodal drug data and to make predictions about the possible types of drug effects.

Authors

  • Zihao Yang
    School of Electrical Engineering, Shenyang University of Technology, Shenyang, China.
  • Kuiyuan Tong
    Faculty of Life Science and Food Engineering, Huaiyin Institute of Technology, Huaian, 223003, Jiangsu, China.
  • Shiyu Jin
    Faculty of Life Science and Food Engineering, Huaiyin Institute of Technology, Huaian, 223003, Jiangsu, China.
  • Shiyan Wang
    Faculty of Life Science and Food Engineering, Huaiyin Institute of Technology, Huaian, 223003, Jiangsu, China. shiyanwanghyit@163.com.
  • Chao Yang
    Translational Institute for Cancer Pain, Chongming Hospital Affiliated to Shanghai University of Health & Medicine Sciences (Xinhua Hospital Chongming Branch), Shanghai 202155, P. R. China.
  • Feng Jiang
    Hospital of Minzu University of China, Beijing 100081, China.