Artificial intelligence for neurodegenerative experimental models

Area of Science:

• Neuroscience and Experimental Medicine
• Computational Biology and Bioinformatics

Background:

• Experimental models are crucial for neurodegenerative disease research, but translating findings to human clinical trials faces significant challenges, leading to high failure rates.
• Reproducibility and cross-species translation are persistent issues in preclinical dementia research, hindering the development of effective treatments.

Purpose of the Study:

• To review the application of artificial intelligence (AI) and machine learning (ML) in experimental medicine for dementia research.
• To highlight best practices and resources for quantifying and evaluating translatability in preclinical dementia models.
• To evaluate how AI and ML can enhance cross-model reproducibility and translation to human biology while maintaining interpretability.

Main Methods:

• Systematic review of AI and ML applications in experimental medicine for dementia.
• Analysis of challenges in reproducibility, cross-species translation, and data curation.
• Identification of data resources and AI approaches to address these challenges.

Main Results:

• AI and ML approaches are emerging in experimental medicine but are currently in their early stages.
• Specific issues identified include poor reproducibility, difficulties in cross-species translation, and inadequate data curation.
• AI, particularly multi-omics analysis, shows potential for improving drug discovery and strengthening preclinical research if based on robust data.

Conclusions:

• AI and ML hold significant promise for advancing dementia research by improving the reliability and translatability of experimental findings.
• The successful implementation of AI/ML requires high-quality, reproducible experimental data and effective data curation strategies.
• Future applications of AI in multi-omics analysis offer exciting possibilities for novel therapeutic strategies in dementia drug discovery.