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Machine learning and its applications in plant molecular studies.

Shanwen Sun1, Chunyu Wang2, Hui Ding3

  • 1University of Bayreuth in Germany. He is now a postdoctoral fellow at the Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China.

Briefings in Functional Genomics
|December 24, 2019
PubMed
Summary

Machine learning offers powerful tools for analyzing vast plant genomic data. This study provides resources and methods to help plant biologists utilize machine learning for gene discovery and stress resistance research.

Keywords:
evaluation metricsgenomicsplantssupervised machine learningunsupervised machine learning

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • High-throughput sequencing generates massive plant genomic data, posing analysis challenges.
  • Limited familiarity with machine learning (ML) restricts its application in plant molecular biology.
  • Current ML use in plants is confined to a few species and algorithms.

Purpose of the Study:

  • To provide foundational steps for developing ML frameworks in plant genomics.
  • To offer a comprehensive overview of ML algorithms and evaluation metrics for plant biologists.
  • To facilitate the application of ML in plant molecular studies.

Main Methods:

  • Development of ML frameworks for genomic data analysis.
  • Compilation of ML algorithms and evaluation metrics.
  • Identification of curated plant genomic data sources and R packages.
  • Review of current ML applications in identifying stress resistance genes.

Main Results:

  • A guide for constructing ML frameworks is presented.
  • A broad spectrum of ML algorithms and metrics is detailed.
  • Resources for accessing plant genomic data and relevant R packages are provided.
  • Examples of ML in identifying biotic and abiotic stress resistance genes are discussed.

Conclusions:

  • This study empowers plant biologists to adopt ML for genomic data analysis.
  • Facilitating ML adoption will accelerate discoveries in plant molecular biology.
  • Increased use of ML and plant sequencing data will advance the field significantly.