ADAM-1: An AI Reasoning and Bioinformatics Model for Alzheimer's Disease Detection and Microbiome-Clinical Data...

Area of Science:

• Computational biology
• Artificial intelligence in medicine
• Neuroscience

Background:

• Alzheimer's disease (AD) diagnosis relies on complex multimodal data.
• Existing analytical models may lack robustness and consistency.
• Integrating diverse data sources is crucial for enhanced AD understanding.

Purpose of the Study:

• To introduce Alzheimer's Disease Analysis Model Generation 1 (ADAM-1), a novel large language model (LLM) framework.
• To leverage multimodal data for improved Alzheimer's disease classification.
• To enhance AD research and diagnostic applications through advanced AI.

Main Methods:

• Development of ADAM-1, a multi-agent reasoning LLM framework.
• Integration and analysis of microbiome profiles, clinical datasets, and knowledge bases.
• Comparative evaluation against XGBoost using multimodal human biological data.

Main Results:

• ADAM-1 demonstrated a significantly improved mean F1 score compared to XGBoost.
• ADAM-1 exhibited significantly reduced variance, indicating enhanced robustness and consistency.
• The framework effectively contextualizes findings with literature-driven evidence.

Conclusions:

• ADAM-1 offers a robust and consistent approach for Alzheimer's disease classification.
• The LLM framework shows potential for broader applications in AD research and diagnostics.
• Future work will expand data modalities and predictive capabilities for disease progression.