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This systematic review examines the use of machine learningMachine learning methods for DNA sequenceDNA sequences classification to evaluate their performance, note challenges, and point toward future potential. 40 studies were eligible, and their characteristics and outcomes were synthesized to compare the performance of different algorithms. Evidence shows that machine learningMachine learning methods have great promise to improve classification accuracy, although study bias and inconsistency remain challenges. Overall, the review points to the growing significance of machine learningMachine learning in genomics and what this means for personalized medicine.

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Systematic Review on the Enhancement of DNA Sequence Classification Through Machine Learning and Deep Learning Techniques

  • 会议论文

  • 发表日期：

    2026年

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  • Elias Tabane,
  • Ernest Mnkandla,
  • Zenghui Wang

摘要

This systematic review examines the use of machine learningMachine learning methods for DNA sequenceDNA sequences classification to evaluate their performance, note challenges, and point toward future potential. 40 studies were eligible, and their characteristics and outcomes were synthesized to compare the performance of different algorithms. Evidence shows that machine learningMachine learning methods have great promise to improve classification accuracy, although study bias and inconsistency remain challenges. Overall, the review points to the growing significance of machine learningMachine learning in genomics and what this means for personalized medicine.