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Front Genet. 2019 Jan 29;10:13. doi: 10.3389/fgene.2019.00013. eCollection 2019.

deepDriver: Predicting Cancer Driver Genes Based on Somatic Mutations Using Deep Convolutional Neural Networks.

Luo P1, Ding Y1, Lei X2, Wu FX1,3,4,5.

Author information

1
Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK, Canada.
2
School of Computer Science, Shaanxi Normal University, Xian, China.
3
School of Mathematics and Statistics, Hainan Normal University, Haikou, China.
4
Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK, Canada.
5
Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada.

Abstract

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 scores of 0.984 and 0.976 on breast cancer and colorectal cancer, which are superior to the competing algorithms. Further evaluations of the top 10 predictions also demonstrate that deepDriver is valuable for predicting new driver genes.

KEYWORDS:

cancer mutations; convolutional neural networks; deep learning; driver gene prediction; gene similarity network

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