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人工智能在肌萎缩侧索硬化症生物标志物发现及治疗策略开发中的作用与潜力

Role and Potential of Artificial Intelligence in Biomarker Discovery and Development of Treatment Strategies for Amyotrophic Lateral Sclerosis.

作者信息

Kitaoka Yoshihiro, Uchihashi Toshihiro, Kawata So, Nishiura Akira, Yamamoto Toru, Hiraoka Shin-Ichiro, Yokota Yusuke, Isomura Emiko Tanaka, Kogo Mikihiko, Tanaka Susumu, Spigelman Igor, Seki Soju

机构信息

Laboratory of Neuropharmacology, Section of Biosystems and Function, School of Dentistry, University California, Los Angeles, 714 Tiverton, Los Angeles, CA 90095, USA.

Department of Oral and Maxillofacial Surgery, Graduate School of Dentistry, The University of Osaka, Yamadaoka, Suita 565-0871, Japan.

出版信息

Int J Mol Sci. 2025 May 2;26(9):4346. doi: 10.3390/ijms26094346.

Abstract

Neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS), present significant challenges owing to their complex pathologies and a lack of curative treatments. Early detection and reliable biomarkers are critical but remain elusive. Artificial intelligence (AI) has emerged as a transformative tool, enabling advancements in biomarker discovery, diagnostic accuracy, and therapeutic development. From optimizing clinical-trial designs to leveraging omics and neuroimaging data, AI facilitates understanding of disease and treatment innovation. Notably, technologies such as AlphaFold and deep learning models have revolutionized proteomics and neuroimaging, offering unprecedented insights into ALS pathophysiology. This review highlights the intersection of AI and ALS, exploring the current state of progress and future therapeutic prospects.

摘要

神经退行性疾病,包括肌萎缩侧索硬化症(ALS),由于其复杂的病理状况和缺乏治愈性治疗方法,带来了重大挑战。早期检测和可靠的生物标志物至关重要,但仍然难以捉摸。人工智能(AI)已成为一种变革性工具,推动了生物标志物发现、诊断准确性和治疗开发方面的进展。从优化临床试验设计到利用组学和神经影像数据,人工智能有助于理解疾病和治疗创新。值得注意的是,诸如AlphaFold等技术和深度学习模型已经彻底改变了蛋白质组学和神经影像,为ALS病理生理学提供了前所未有的见解。本综述重点介绍了人工智能与ALS的交叉点,探讨了当前的进展状况和未来的治疗前景。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6920/12072360/e1cb89f7830b/ijms-26-04346-g001.jpg

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