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Mathematical Biosciences and Engineering

2023, Volume 20, Issue 12: 21643-21669. doi: 10.3934/mbe.2023958
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Advances in computational methods for identifying cancer driver genes

  • 1.
    School of Computer Science and Engineering, Changshu Institute of Technology, Changshu 215500, China
  • 2.
    School of Textile Garment and Design, Changshu Institute of Technology, Changshu 215500, China
  • 3.
    School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou 213032, China

  • Academic Editor: Jinshan Tang
  • Received: 27 August 2023 Revised: 19 November 2023 Accepted: 22 November 2023 Published: 06 December 2023

  • Cancer driver genes (CDGs) are crucial in cancer prevention, diagnosis and treatment. This study employed computational methods for identifying CDGs, categorizing them into four groups. The major frameworks for each of these four categories were summarized. Additionally, we systematically gathered data from public databases and biological networks, and we elaborated on computational methods for identifying CDGs using the aforementioned databases. Further, we summarized the algorithms, mainly involving statistics and machine learning, used for identifying CDGs. Notably, the performances of nine typical identification methods for eight types of cancer were compared to analyze the applicability areas of these methods. Finally, we discussed the challenges and prospects associated with methods for identifying CDGs. The present study revealed that the network-based algorithms and machine learning-based methods demonstrated superior performance.

    Citation: Ying Wang, Bohao Zhou, Jidong Ru, Xianglian Meng, Yundong Wang, Wenjie Liu. Advances in computational methods for identifying cancer driver genes[J]. Mathematical Biosciences and Engineering, 2023, 20(12): 21643-21669. doi: 10.3934/mbe.2023958

    Related Papers:

  • Cancer driver genes (CDGs) are crucial in cancer prevention, diagnosis and treatment. This study employed computational methods for identifying CDGs, categorizing them into four groups. The major frameworks for each of these four categories were summarized. Additionally, we systematically gathered data from public databases and biological networks, and we elaborated on computational methods for identifying CDGs using the aforementioned databases. Further, we summarized the algorithms, mainly involving statistics and machine learning, used for identifying CDGs. Notably, the performances of nine typical identification methods for eight types of cancer were compared to analyze the applicability areas of these methods. Finally, we discussed the challenges and prospects associated with methods for identifying CDGs. The present study revealed that the network-based algorithms and machine learning-based methods demonstrated superior performance.


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