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This article is part of the Research Topic

Deep Learning for Toxicity and Disease Prediction

Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Genet. | doi: 10.3389/fgene.2019.00013

deepDriver: predicting cancer driver genes by convolutional neural networks

 Ping Luo1, Yulian Ding1, Xiujuan Lei2 and  FangXiang Wu1*
  • 1University of Saskatchewan, Canada
  • 2Shaanxi Normal University, China

With the advances in high-throughput technologies, millions of somatic mutations have been reported in the past decade. Identifying driver genes with oncogenic mutations from these data is a critical and challenging problem. Many computational methods have been proposed to predict driver genes. Among them, machine learning-based methods usually train a classifier with representations that concatenate various types of features extracted from different kinds of data. Although successful, simply concatenating different types of features may not be the best way to fuse these data. We notice that a few types of data characterize the similarities of genes, to better integrate them with other data and improve the accuracy of driver gene prediction, in this study, a deep learning-based method (deepDriver) is proposed by performing convolution on mutation-based features of genes and their neighbors in the similarity networks. The method allows the convolutional neural network to learn information within mutation data and similarity networks simultaneously, which enhances the prediction of driver genes. deepDriver achieves AUC values of 0.984 and 0.976 on breast cancer and colorectal cancer, which are superior to the competing algorithms. Further evaluations on the top 10 predictions also demonstrate that deepDriver is valuable for predicting new driver genes.

Keywords: deep learning, Convolutional Neural Networks, driver gene prediction, cancer mutations, gene similarity network

Received: 02 Oct 2018; Accepted: 11 Jan 2019.

Edited by:

Chaoyang Zhang, University of Southern Mississippi, United States

Reviewed by:

Luis Rueda, University of Windsor, Canada
Chao Xu, University of Oklahoma Health Sciences Center, United States  

Copyright: © 2019 Luo, Ding, Lei and Wu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Prof. FangXiang Wu, University of Saskatchewan, Saskatoon, S7N 5A2, Saskatchewan, Canada,