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Artificial Intelligence in Medicine: Microbiome-Based Machine Learning for Phenotypic Classification.

Xi Cheng1, Bina Joe2

  • 1Bioinformatics & Artificial Intelligence Laboratory, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, OH, USA. Xi.Cheng@utoledo.edu.

Methods in Molecular Biology (Clifton, N.J.)
|May 31, 2023
PubMed
Summary
This summary is machine-generated.

Machine learning models analyze complex microbiome data for phenotypic classification in life sciences. This chapter details experimental procedures for applying these advanced computational approaches.

Keywords:
ClassificationDiagnosisDiseaseMachine learningMicrobiomePhenotype

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Area of Science:

  • Computational biology
  • Microbiome research
  • Artificial intelligence in healthcare

Background:

  • Life sciences and healthcare generate vast, complex datasets, including microbiome data.
  • Advanced computational methods, particularly machine learning, are crucial for analyzing this data.
  • Phenotypic classification is a key application area for these analytical techniques.

Purpose of the Study:

  • To describe experimental procedures for utilizing microbiome-based machine learning models.
  • To facilitate the application of artificial intelligence in analyzing biological data for classification tasks.

Main Methods:

  • Utilizing machine learning algorithms tailored for microbiome data analysis.
  • Implementing computational approaches for large-scale biological data processing.
  • Developing experimental protocols for phenotypic classification using microbial data.

Main Results:

  • The chapter provides a procedural guide for microbiome-based machine learning.
  • It outlines methods for translating complex biological data into actionable classifications.

Conclusions:

  • Microbiome-based machine learning offers a powerful approach for phenotypic classification.
  • The described experimental procedures enable the application of AI in life sciences and healthcare.
  • Advanced computational strategies are essential for leveraging complex biological datasets.