Analysis of Microbiome for AP and CRC Discrimination.

Area of Science:

• Microbiome research
• Bioinformatics
• Computational biology

Background:

• Microbiome data analysis is crucial for understanding human health.
• Limited data availability impedes microbiome research progress.
• Synthetic data generation offers a potential solution to data scarcity.

Purpose of the Study:

• To enrich an unbalanced microbiome dataset using machine learning.
• To explore the use of synthetic data for improving adenomatous polyps (AP) and colorectal cancer (CRC) sample classification.
• To identify key microbial operational taxonomic units (OTUs) for distinguishing between AP and CRC patients.

Main Methods:

• Utilized the Synthetic Data Vault Python library for Gaussian Copula-based data synthesis.
• Evaluated synthetic data quality using logistic regression and support vector machine classifiers.
• Applied layer-wise relevance propagation (LRP) on a deep learning model to identify discriminative OTU features.
• Trained and tested machine learning classifiers on enriched and simplified datasets.

Main Results:

• Successfully generated high-quality synthetic microbiome data comparable to real samples.
• Identified key bacterial taxa with high discriminatory power between AP and CRC patients.
• Demonstrated the potential of synthetic data enrichment to improve classification accuracy.
• Extracted simplified OTU features for enhanced microbiome analysis.

Conclusions:

• Machine learning and synthetic data enrichment are powerful tools for advancing microbiome research.
• This approach can enhance classification accuracy and reveal novel microbial biomarkers.
• The identified microbial markers hold potential for clinical diagnostic and prognostic applications in AP and CRC.