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

Journal: International journal of molecular sciences
Published Date: May 2, 2025

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.

Authors

  • Yoshihiro Kitaoka
    Laboratory of Neuropharmacology, Section of Biosystems and Function, School of Dentistry, University California, Los Angeles, 714 Tiverton, Los Angeles, CA 90095, USA.
  • Toshihiro Uchihashi
    Department of Oral and Maxillofacial Surgery, Graduate School of Dentistry, The University of Osaka, Yamadaoka, Suita 565-0871, Japan.
  • So Kawata
    Department of Oral and Maxillofacial Surgery, Graduate School of Dentistry, The University of Osaka, Yamadaoka, Suita 565-0871, Japan.
  • Akira Nishiura
    Department of Oral and Maxillofacial Surgery, Graduate School of Dentistry, The University of Osaka, Yamadaoka, Suita 565-0871, Japan.
  • Toru Yamamoto
    Division of Dental Anesthesiology, Faculty of Dentistry, Graduate School of Medicine and Dental Sciences, Niigata University, Niigata 951-8514, Japan.
  • Shin-Ichiro Hiraoka
    Department of Oral and Maxillofacial Surgery, Graduate School of Dentistry, Osaka University, 1-8 Yamada-Oka, 565-0871, Suita, Osaka, Japan. hirashins2@gmail.com.
  • Yusuke Yokota
    National Institute of Information and Communications Technology (NICT), Advanced ICT Research Institute, Kobe, Japan.
  • Emiko Tanaka Isomura
    Department of Oral and Maxillofacial Surgery, Graduate School of Dentistry, The University of Osaka, Yamadaoka, Suita 565-0871, Japan.
  • Mikihiko Kogo
    Department of Oral and Maxillofacial Surgery, Graduate School of Dentistry, The University of Osaka, Yamadaoka, Suita 565-0871, Japan.
  • Susumu Tanaka
    Department of Oral and Maxillofacial Surgery, Graduate School of Dentistry, Osaka University, 1-8 Yamada-Oka, 565-0871, Suita, Osaka, Japan.
  • Igor Spigelman
    Laboratory of Neuropharmacology, Section of Biosystems and Function, School of Dentistry, University California, Los Angeles, 714 Tiverton, Los Angeles, CA 90095, USA.
  • Soju Seki
    Department of Oral and Maxillofacial Surgery, Graduate School of Dentistry, The University of Osaka, Yamadaoka, Suita 565-0871, Japan.

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