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Identifying and ranking potential cancer drivers using representation learning on attributed network.

Wei Peng1, Sichen Yi2, Wei Dai1

  • 1Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650050, China; Computer Technology Application Key Lab of Yunnan Province, Kunming University of Science and Technology, Kunming 650050, China.

Methods (San Diego, Calif.)
|August 8, 2020
PubMed
Summary
This summary is machine-generated.

Identifying cancer driver genes is crucial for targeted therapies. This study introduces a novel Representation Learning on Attributed Graphs (RLAG) method, outperforming existing approaches in predicting lung, breast, and prostate cancer drivers.

Keywords:
Attributed network embeddingCancer driverNetwork representation learning

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Cancer development is driven by accumulating genomic alterations, with only a subset of mutations acting as drivers.
  • Accurate identification of cancer driver genes is critical for advancing drug design, diagnostics, and treatment strategies.

Purpose of the Study:

  • To introduce a novel method, Representation Learning on Attributed Graphs (RLAG), for identifying potential cancer driver genes.
  • To leverage both network structure and node attributes for gene feature representation in cancer driver prediction.

Main Methods:

  • Developed RLAG, a novel approach integrating network topology and gene attributes for feature learning.
  • Applied feature vectors to subgroup genes and ranked potential drivers based on intrinsic properties and subgroup importance.
  • Validated the method on lung, breast, and prostate cancer datasets.

Main Results:

  • The RLAG method demonstrated superior performance compared to three state-of-the-art methods.
  • Achieved higher Precision, Recall, and F1-score values in predicting cancer driver genes.
  • Successfully identified potential driver genes across multiple cancer types.

Conclusions:

  • RLAG offers a robust and effective approach for identifying cancer driver genes.
  • The method's ability to integrate network and attribute data enhances prediction accuracy.
  • This advancement holds promise for improving cancer diagnostics and therapeutic development.