神经系统疾病多组学分析中的人工智能

摘要(中文译文)

人工智能（AI）整合多组学数据正在迅速改变神经疾病研究的格局。随着我们对阿尔茨海默病、帕金森病、多发性硬化症和精神分裂症等复杂脑部疾病认识的不断深入，对处理和解读高维生物学数据的精密工具的需求日益增长。在本章中，我们探讨了AI与多组学在神经生物学中的整合，描述了AI技术从经典的基于规则的系统到前沿深度学习框架和基础模型的演变。我们提供了一份全面的指南，将特定的组学组合与适当的AI方法相匹配，以回答有针对性的神经科学问题，讨论了主要的神经系统疾病专用数据库和资源，并呈现了详细的案例研究，突出这些方法在现实世界中的潜力和临床转化前景。此外，我们还探讨了成功实现临床转化所必须克服的现有技术、伦理和可解释性挑战。通过本章的学习，读者将同时具备理论知识和实践指导，以便在多组学神经疾病研究中选择和应用合适的AI工具。

Abstract

The integration of multi-omics data using Artificial Intelligence (AI) is rapidly transforming the landscape of neurological disease research. As our understanding of complex brain disorders such as Alzheimer's disease, Parkinson's disease, multiple sclerosis, and schizophrenia deepens, there is an ever-increasing demand for sophisticated tools for handling and interpreting high-dimensional biological data. In this chapter, we explore the integration of AI and multi-omics in neurobiology, describing the evolution of AI techniques from classical rule-based systems to cutting-edge deep learning frameworks and foundation models. We provide a comprehensive guide to matching specific omics combinations with appropriate AI methods to answer targeted neuroscientific questions, discuss major neurological disease-specific databases and resources, and present detailed case studies that highlight the real-world potential and clinical translation of these approaches. Additionally, we address the existing technical, ethical, and interpretability challenges that must be overcome for successful clinical implementation. By the end of this chapter, readers will be equipped with both theoretical knowledge and practical guidance to choose and apply appropriate AI tools in multi-omics neurological disease research.

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