Microbes and complex diseases: from experimental results to computational models.

Area of Science:

• Microbiome research
• Computational biology
• Disease pathogenesis

Background:

• Human microbes outnumber human cells, playing vital roles in immunity, digestion, and metabolism.
• Emerging evidence links human microbes to noncommunicable diseases, offering new insights into disease origins.
• Understanding microbe-disease links is key for novel diagnostic, treatment, and drug development strategies.

Purpose of the Study:

• To review computational models for predicting microbe-disease associations.
• To introduce various computational approaches, including score function, network, machine learning, and experimental analysis-based models.
• To summarize the strengths and weaknesses of these models and suggest future research directions.

Main Methods:

• Introduction to microbes, relevant databases, and web servers.
• Detailed explanation of four categories of computational models: score function-based, network algorithm-based, machine learning-based, and experimental analysis-based models.
• Comparative analysis of the advantages and disadvantages of each model type.

Main Results:

• Computational models provide a more efficient and less costly alternative to experimental methods for identifying microbe-disease associations.
• Various computational models exist, each with unique strengths and limitations for predicting potential disease-related microbes.
• The study highlights the potential of computational approaches for large-scale predictions in this field.

Conclusions:

• Computational models are essential tools for predicting microbe-disease associations, complementing experimental research.
• Further development and application of these models will accelerate the understanding of disease mechanisms and the discovery of new therapies.
• The future of microbiome research lies in leveraging computational power for large-scale, accurate predictions of microbe-disease links.