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Discovering mutated driver genes through a robust and sparse co-regularized matrix factorization framework with prior

Jianing Xi1, Minghui Wang2,3, Ao Li1,4

  • 1School of Information Science and Technology, University of Science and Technology of China, Huangshan Road, Hefei, 230027, China.

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This study introduces a novel matrix factorization method to identify low-frequency mutated driver genes in cancer. By integrating gene interaction networks and mRNA expression data, the approach enhances driver gene discovery and outperforms existing methods.

Keywords:
BioinformaticsCancerDriver geneMatrix factorizationNetwork regularization

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Identifying mutated driver genes is crucial for understanding tumorigenesis.
  • Existing network-based methods for driver gene discovery often overlook valuable mRNA expression data.
  • mRNA expression patterns are highly informative for cancer progression.

Purpose of the Study:

  • To develop a method that integrates both interaction network and mRNA expression data for driver gene discovery.
  • To improve the accuracy and robustness of identifying low-frequency mutated driver genes.
  • To address the limitations of existing network-based approaches.

Main Methods:

  • Proposed a robust and sparse co-regularized nonnegative matrix factorization framework.
  • Incorporated prior information from gene interaction networks and mRNA expression patterns.
  • Utilized Frobenius norm regularization to prevent overfitting and sparsity-inducing penalties for gene selection.

Main Results:

  • The proposed method effectively leverages both interaction network and mRNA expression data.
  • Evaluation experiments demonstrated superior performance compared to existing network-based methods.
  • The method successfully identified driver genes missed by competing approaches.

Conclusions:

  • The developed method enhances driver gene discovery by integrating diverse biological data.
  • The robust and sparse co-regularized matrix factorization framework offers improved performance.
  • This approach provides a valuable tool for cancer research and personalized medicine.