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

Ping Luo1, Yulian Ding1, Xiujuan Lei2

  • 1Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK, Canada.

Frontiers in Genetics
|February 15, 2019
PubMed
Summary

Identifying cancer driver genes is crucial. A new deep learning method, deepDriver, effectively integrates mutation and gene similarity data, outperforming existing algorithms in predicting oncogenic mutations.

Keywords:
cancer mutationsconvolutional neural networksdeep learningdriver gene predictiongene similarity network

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Area of Science:

  • Genomics
  • Computational Biology
  • Cancer Research

Background:

  • High-throughput technologies have generated millions of somatic mutation data.
  • Identifying driver genes from mutation data is a critical challenge in cancer research.
  • Current machine learning methods often concatenate features, potentially limiting data integration.

Purpose of the Study:

  • To propose a novel deep learning-based method, deepDriver, for improved driver gene prediction.
  • To enhance the integration of mutation data with gene similarity networks.
  • To accurately identify genes with oncogenic mutations.

Main Methods:

  • Developed deepDriver, a deep learning model utilizing convolutional neural networks.
  • Applied convolution on mutation-based features and gene neighbors within similarity networks.
  • Simultaneously learned from mutation data and gene similarity networks.

Main Results:

  • DeepDriver achieved superior Area Under the Curve (AUC) scores: 0.984 for breast cancer and 0.976 for colorectal cancer.
  • Performance surpassed competing driver gene prediction algorithms.
  • Top 10 predictions from deepDriver showed high potential for identifying novel driver genes.

Conclusions:

  • DeepDriver offers an effective approach for driver gene prediction by integrating diverse data types.
  • The method demonstrates significant improvements in accuracy and potential for discovering new cancer-related genes.
  • This deep learning strategy enhances the analysis of somatic mutations for cancer research.