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MLGCN-Driver: a cancer driver gene identification method based on multi-layer graph convolutional neural network.

Pi-Jing Wei1, Jingxin Zhou1, Rui-Fen Cao2

  • 1Key Laboratory of Intelligent Computing Signal Processing of Ministry of Education, Institute of Physical Science and Information Technology, Anhui University, 111 Jiulong Road, Hefei, 230601, Anhui, China.

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Identifying cancer driver genes is crucial for understanding cancer progression. This study introduces MLGCN-Driver, a novel method using multi-layer graph convolutional networks to effectively identify driver genes by analyzing high-order network features.

Keywords:
Cancer driver geneMulti-layer graph convolutional neural networksMulti-omics feature

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Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Cancer progression is driven by mutations in driver genes.
  • Identifying cancer driver genes is a key research area.
  • Existing methods often overlook high-order network features.

Purpose of the Study:

  • To propose a novel method, MLGCN-Driver, for enhanced cancer driver gene identification.
  • To incorporate high-order network features into driver gene prediction.
  • To leverage multi-omics and topological network data.

Main Methods:

  • Developed MLGCN-Driver, a multi-layer graph convolutional neural network (GCN) model.
  • Utilized initial residual connections and identity mappings to learn multi-omics features.
  • Employed the node2vec algorithm to extract topological structure features.
  • Integrated biological and topological features for driver gene probability calculation.

Main Results:

  • MLGCN-Driver effectively learns from biological multi-omics and network topological features.
  • Residual connections and identity mappings prevent feature over-smoothing.
  • The method calculates driver gene probabilities based on integrated features.

Conclusions:

  • MLGCN-Driver demonstrates superior performance in driver gene identification on pan-cancer and specific cancer datasets.
  • The method achieves excellent results in Area Under the ROC Curve (AUC) and Area Under the Precision-Recall Curve (AUPRC).
  • MLGCN-Driver outperforms state-of-the-art approaches in driver gene identification.