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<p>Machine learning is a robust framework to analyze questions using complex data in a variety of fields. We present definitions and recent applications of four key machine learning methods and discuss their advantages and challenges in biological research. Through a set of systematically selected case studies, we highlight how machine learning models have been used in a range of applications, including phylogenomics, disease prediction, and host taxonomy prediction. We identify additional potential areas of integration of machine learning into questions with biological relevance. This intersection can be further enhanced through collaboration and innovation on parallelization, interpretability, and preprocessing.</p>

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Machine learning in biological research: key algorithms, applications, and future directions

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月29日

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  • Md Nafis Ul Alam,
  • Kiran Basava,
  • Ani Chitransh,
  • H. M. Abdul Fattah,
  • Hector D. Garcia-Verdugo,
  • Shih-Hsuan Lo,
  • Tanisha Lohchab,
  • Kristen M. Martinet,
  • Cristian Román-Palacios,
  • Jhan Carlos Salazar,
  • Danielle Van Boxel

摘要

Machine learning is a robust framework to analyze questions using complex data in a variety of fields. We present definitions and recent applications of four key machine learning methods and discuss their advantages and challenges in biological research. Through a set of systematically selected case studies, we highlight how machine learning models have been used in a range of applications, including phylogenomics, disease prediction, and host taxonomy prediction. We identify additional potential areas of integration of machine learning into questions with biological relevance. This intersection can be further enhanced through collaboration and innovation on parallelization, interpretability, and preprocessing.