Application of Deep Learning in Plant-Microbiota Association Analysis

Area of Science:

• Microbiome research
• Plant science
• Computational biology

Background:

• Understanding plant-microbiome interactions is crucial for agricultural advancements.
• Analyzing microbiome data presents challenges in species identification and model selection.
• Computational models are essential for dissecting the relationship between microbiomes and plant hosts.

Purpose of the Study:

• To review analytical strategies for microbiome data analysis.
• To describe the application of deep learning models in plant-microbiome correlation studies.
• To discuss model adaptation for critical data processing steps.

Main Methods:

• Review of existing literature on microbiome data analysis strategies.
• Exploration of deep learning methodologies for handling complex, high-dimensional microbiome data.
• Case studies illustrating the application of various deep learning models in plant-microbiome correlation.

Main Results:

• Deep learning methods excel at managing complex, sparse, noisy, and high-dimensional microbiome data.
• Feature representation and pattern recognition are key advantages of deep learning in association analysis.
• Convolutional neural networks and graph neural networks are recommended for plant microbiome analysis.

Conclusions:

• Deep learning models are well-suited for plant microbiome data analysis due to their adaptability in data processing, structure, and operating principles.
• These models offer powerful tools for interpreting plant-microbiome associations.
• Specific deep learning architectures like CNNs and GNNs show significant potential for advancing plant microbiome research.