A review of machine learning methods for cancer characterization from microbiome data

Area of Science:

• Microbiome research
• Computational biology
• Oncology

Background:

• The human microbiome influences cancer development, progression, and treatment response.
• Microbiome-based cancer characterization holds potential for novel diagnostic and prognostic tools.
• Complex microbial signatures necessitate advanced computational approaches like Machine Learning (ML).

Purpose of the Study:

• To review ML methodologies for cancer characterization using microbiome data.
• To highlight critical considerations in sample collection, feature selection, and data pre-processing.
• To guide ML model selection, validation, and address current limitations for clinical translation.

Main Methods:

• Review of existing literature on ML applications in microbiome-based cancer research.
• Discussion of data pre-processing techniques and their impact on ML model performance.
• Analysis of ML model selection criteria, validation strategies, and common pitfalls.

Main Results:

• ML methods, particularly Random Forests, show promise but often lack generalizability for clinical use.
• Conflicting results across studies are frequently attributed to poor model generalizability.
• Current approaches require refinement to overcome limitations and achieve reliable clinical application.

Conclusions:

• Enhancing ML model generalizability is crucial for integrating microbiome data into clinical cancer care.
• Future directions include using larger datasets, advanced deep learning, and microbiome-specific ML models.
• Addressing technical artifacts and exploring non-taxonomical data representations will improve model performance.