Predicting drug-target interaction network using deep learning model.

Journal: Computational biology and chemistry
Published Date: Mar 25, 2019

Abstract

BACKGROUND: Traditional methods for drug discovery are time-consuming and expensive, so efforts are being made to repurpose existing drugs. To find new ways for drug repurposing, many computational approaches have been proposed to predict drug-target interactions (DTIs). However, due to the high-dimensional nature of the data sets extracted from drugs and targets, traditional machine learning approaches, such as logistic regression analysis, cannot analyze these data sets efficiently. To overcome this issue, we propose LASSO (Least absolute shrinkage and selection operator)-based regularized linear classification models and a LASSO-DNN (Deep Neural Network) model based on LASSO feature selection to predict DTIs. These methods are demonstrated for repurposing drugs for breast cancer treatment.

Authors

  • Jiaying You
    Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, Manitoba, Canada; Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, Manitoba, Canada; George & Fay Yee Centre for Healthcare Innovation, University of Manitoba, Winnipeg, Manitoba, Canada.
  • Robert D McLeod
    Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, Manitoba, Canada.
  • Pingzhao Hu
    c Department of Biochemistry and Medical Genetics , University of Manitoba , Winnipeg , MB , Canada.

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