Comparative study of classifiers for human microbiome data.

Area of Science:

• Microbiome Research
• Computational Biology
• Machine Learning Applications

Background:

• Commensal microorganisms are crucial for human health and disease.
• Microbiome dysbiosis is linked to numerous diseases.
• Supervised classification can identify disease-discriminative microbial signatures.

Purpose of the Study:

• To systematically compare the performance of four classification methods on human microbiome data.
• To evaluate Random Forests (RF), eXtreme Gradient Boosting (XGBoost), Elastic Net (ENET), and Support Vector Machine (SVM) for microbiome-based disease classification.

Main Methods:

• Comparative analysis of RF, XGBoost, ENET, and SVM.
• Utilized 29 benchmark human microbiome datasets for classification tasks.
• Assessed classification performance and feature selection overlap.

Main Results:

• XGBoost demonstrated superior performance on a limited number of datasets.
• XGBoost, RF, and ENET exhibited comparable performance across most datasets.
• XGBoost had significantly longer training times due to extensive hyperparameters.

Conclusions:

• The choice of classifier impacts microbiome data analysis and disease prediction.
• While feature importance partially overlaps, classification performance differences are minimal.
• Further research into optimized classifier selection for microbiome studies is warranted.