Robust biomarker discovery for microbiome-wide association studies.

Area of Science:

• Microbiome Research
• Bioinformatics
• Computational Biology

Background:

• High-throughput sequencing generates massive microbiome data, necessitating advanced analytical methods.
• Microbiome-wide association studies (MWAS) explore links between the microbiome and health/environment.
• Deep Neural Networks (DNNs) show promise but are data-intensive and lack transparency for MWAS.

Purpose of the Study:

• To introduce a stable and robust deep learning model for microbiome-wide association studies.
• To develop an ensemble feature selection method for identifying microbial biomarkers.
• To facilitate the discovery of microbial biomarkers for disease diagnosis.

Main Methods:

• Implementation of a Deep Forest model for MWAS.
• Development of an ensemble feature selection approach.
• Experimental validation of model stability and robustness.

Main Results:

• The proposed ensemble feature selection method based on Deep Forest demonstrated significant stability and robustness.
• The method effectively guides the identification of microbial biomarkers.
• Successful application in microbiome-wide association studies.

Conclusions:

• Deep Forest offers a practical deep learning solution for MWAS, overcoming DNN limitations.
• The ensemble feature selection method aids in discovering clinically relevant microbial biomarkers.
• This approach supports the diagnosis of microbiome-related diseases.