Compositionality, sparsity, spurious heterogeneity, and other data-driven challenges for machine learning algorithms...

Area of Science:

• Plant science
• Microbiome research
• Computational biology

Background:

• The plant-associated microbiome is vital for plant health, growth, and productivity.
• Machine learning (ML) offers powerful tools for understanding complex microbiome interactions.
• High-throughput sequencing data presents unique challenges for ML model performance.

Purpose of the Study:

• To review the challenges of applying ML to plant microbiome data.
• To highlight the impact of data properties (noise, compositionality, sparsity) on ML.
• To assess mitigation strategies for improving ML performance in microbiome studies.

Main Methods:

• Literature review of plant microbiome studies using ML.
• Analysis of data properties impacting ML performance.
• Quantification of mitigation approach effectiveness across different fields.
• Explanation of the mathematical basis for ML performance improvements.

Main Results:

• Many plant microbiome studies using ML overlook critical data properties.
• Data characteristics such as noise, heterogeneity, compositionality, and sparsity negatively affect ML.
• Specific mitigation approaches can significantly enhance ML model performance.
• Accessible analytical packages are emerging for microbiome data analysis.

Conclusions:

• Researchers must be aware of the inherent properties of microbiome datasets.
• Understanding and addressing data challenges are essential for reliable ML in plant microbiome research.
• Proper data handling is key to unlocking the full potential of ML for plant science.