Applications of machine learning in predicting host phenotype based on microbiome

Abstract:

With the advent of the era of big data, how to transform the omics data into easy-to-understand and visualized knowledge is one of the important challenges in bioinformatics. Recently, machine learning techniques had been utilized to analyze the complicated, high-dimensional microbiome data to address the complex mechanisms of human diseases. Here, we firstly summarized microbiome data procession approaches and the most commonly used machine learning algorithms, such as support vector machine (SVM), random forest (RF), and artificial neural networks (ANN). Then, the workflow of machine learning studies was described, and the application of ML algorithms in predicting host phenotypes based on microbiome data was evaluated. Finally, the model construction and validation of machine learning algorithms were demonstrated by using saliva microbiome data to predict oral malodour as an example, and R/Python code for practical data analysis was provided (https://github.com/LiLabZSU/microbioML).