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Biological Sequence Classification: A Review on Data and General Methods.

Chunyan Ao1,2,3, Shihu Jiao2, Yansu Wang3

  • 1School of Computer Science and Technology, Xidian University, Xi'an, China.

Research (Washington, D.C.)
|September 17, 2024
PubMed
Summary
This summary is machine-generated.

Machine learning aids in classifying biological sequences for DNA, RNA, and protein functions. This review organizes diverse classification methods and offers a resource website for biological sequence data analysis.

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Area of Science:

  • Biotechnology
  • Bioinformatics
  • Computational Biology

Background:

  • Exponential growth in biological sequence data necessitates advanced analytical methods.
  • Machine learning (ML) is increasingly applied to biological sequence analysis for predictive modeling.
  • Biological sequence classification is crucial for understanding DNA, RNA, protein, and peptide functions and modifications.

Purpose of the Study:

  • To review and organize machine learning-based classification methods for biological sequences, focusing on function and modification.
  • To provide a centralized resource website with detailed information on classification methods and datasets.
  • To introduce effective model framework construction for biological sequence data and single-cell sequencing analysis.

Main Methods:

  • Literature review of machine learning-based biological sequence classification models.
  • Categorization of diverse classification approaches for biological sequences.
  • Development of a support website to collate information and resources.

Main Results:

  • A comprehensive overview of various biological sequence classification models.
  • A curated website (http://lab.malab.cn/~acy/BioseqData/home.html) offering classification details and dataset links.
  • Introduction to single-cell sequencing data analysis techniques.

Conclusions:

  • Machine learning offers powerful tools for biological sequence classification.
  • A structured approach and accessible resources are needed to navigate the complexity of biological sequence analysis.
  • Future research should address current challenges and explore new perspectives in the field.