Abstract：
Experimental models are essential tools in neurodegenerative disease research. However, the translation of insights and drugs discovered in model systems has proven immensely challenging, marred by high failure rates in human clinical trials.
Here we review the application of artificial intelligence (AI) and machine learning (ML) in experimental medicine for dementia research.
Considering the specific challenges of reproducibility and translation between other species or model systems and human biology in preclinical dementia research, we highlight best practices and resources that can be leveraged to quantify and evaluate translatability. We then evaluate how AI and ML approaches could be applied to enhance both cross-model reproducibility and translation to human biology, while sustaining biological interpretability.
AI and ML approaches in experimental medicine remain in their infancy. However, they have great potential to strengthen preclinical research and translation if based upon adequate, robust, and reproducible experimental data.
There are increasing applications of AI in experimental medicine. We identified issues in reproducibility, cross-species translation, and data curation in the field. Our review highlights data resources and AI approaches as solutions. Multi-omics analysis with AI offers exciting future possibilities in drug discovery.
Here we review the application of artificial intelligence (AI) and machine learning (ML) in experimental medicine for dementia research.
Considering the specific challenges of reproducibility and translation between other species or model systems and human biology in preclinical dementia research, we highlight best practices and resources that can be leveraged to quantify and evaluate translatability. We then evaluate how AI and ML approaches could be applied to enhance both cross-model reproducibility and translation to human biology, while sustaining biological interpretability.
AI and ML approaches in experimental medicine remain in their infancy. However, they have great potential to strengthen preclinical research and translation if based upon adequate, robust, and reproducible experimental data.
There are increasing applications of AI in experimental medicine. We identified issues in reproducibility, cross-species translation, and data curation in the field. Our review highlights data resources and AI approaches as solutions. Multi-omics analysis with AI offers exciting future possibilities in drug discovery.
摘要：
背景：实验模型是神经退行性疾病研究中必不可少的工具。然而,在模型系统中发现的见解和药物的翻译已被证明具有极大的挑战性，在人体临床试验中受到高失败率的损害。
方法：在这里，我们回顾了人工智能（AI）和机器学习（ML）在痴呆症研究的实验医学中的应用。
结果：考虑到临床前痴呆研究中其他物种或模型系统与人类生物学之间的可重复性和翻译的特定挑战，我们重点介绍了可用于量化和评估可译性的最佳实践和资源。然后，我们评估了如何应用AI和ML方法来增强跨模型的可重复性和对人类生物学的翻译。同时保持生物学的可解释性。
结论：实验医学中的AI和ML方法仍处于起步阶段。然而,如果有足够的基础，它们有很大的潜力来加强临床前研究和翻译，健壮,和可重复的实验数据。
结论：AI在实验医学中的应用越来越多。我们发现了再现性方面的问题，跨物种翻译，以及该领域的数据管理。我们的审查重点介绍了数据资源和AI方法作为解决方案。使用AI进行的多组分析为药物发现提供了令人兴奋的未来可能性。
方法：在这里，我们回顾了人工智能（AI）和机器学习（ML）在痴呆症研究的实验医学中的应用。
结果：考虑到临床前痴呆研究中其他物种或模型系统与人类生物学之间的可重复性和翻译的特定挑战，我们重点介绍了可用于量化和评估可译性的最佳实践和资源。然后，我们评估了如何应用AI和ML方法来增强跨模型的可重复性和对人类生物学的翻译。同时保持生物学的可解释性。
结论：实验医学中的AI和ML方法仍处于起步阶段。然而,如果有足够的基础，它们有很大的潜力来加强临床前研究和翻译，健壮,和可重复的实验数据。
结论：AI在实验医学中的应用越来越多。我们发现了再现性方面的问题，跨物种翻译，以及该领域的数据管理。我们的审查重点介绍了数据资源和AI方法作为解决方案。使用AI进行的多组分析为药物发现提供了令人兴奋的未来可能性。
