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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Compare the performance of multiple binary classification models in microbial high-throughput sequencing datasets.

Nuohan Xu1, Zhenyan Zhang1, Yechao Shen1

  • 1College of Environment, Zhejiang University of Technology, Hangzhou, Zhejiang 310032, PR China.

The Science of the Total Environment
|May 10, 2022
PubMed
Summary

This study compared machine learning and deep learning models for predicting microbiota responses to environmental changes. Back propagation neural network (BPNN) and random forest (RF) models demonstrated superior performance for microbiota data analysis.

Keywords:
Deep learningEcotoxicologyMachine learningMetadata analysisMicrobiota

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Area of Science:

  • Microbial Ecology
  • Bioinformatics
  • Computational Biology

Background:

  • Machine learning and deep learning offer tools for predicting microbial community responses to environmental shifts using high-throughput sequencing data.
  • Limited research has compared the performance and practicality of various binary classification models for analyzing complex microbiota datasets.

Purpose of the Study:

  • To evaluate and compare the performance, accuracy, and running time of four binary classification models: random forest (RF), support vector machine (SVM), logistic regression (LR), and back propagation neural network (BPNN).
  • To identify the most suitable algorithms for microbiota data analysis and provide a framework for applying artificial intelligence in microbial ecology.

Main Methods:

  • Pre-processing of microbiota datasets, including removal of low-quality variables and addressing class imbalance.
  • Development and optimization of RF, SVM, LR, and BPNN models.
  • Tuning of model parameters, specifically epochs for BPNN and n_estimators for RF, through iterative adjustments.

Main Results:

  • Dataset pre-processing is crucial for effective model construction.
  • BPNN and RF emerged as the most suitable methods for building microbiota binary classification models.
  • BPNN exhibited the highest accuracy and most robust performance, followed closely by RF, across multiple microbial datasets.

Conclusions:

  • The study provides a comparative analysis of machine learning and deep learning models for microbiota data.
  • BPNN and RF are recommended for predicting microbial responses, with BPNN offering superior accuracy.
  • The findings offer a roadmap for utilizing artificial intelligence in microbial ecology research.