Classification of ALS molecular subtypes: a literature review on machine learning applications and their clinical value

TL;DR

This paper reviews how machine learning is used to classify molecular subtypes of ALS, aiming to improve understanding and treatment of this complex disease.

Contribution

The paper provides a critical review of machine learning applications in identifying ALS molecular subtypes and their clinical relevance.

Findings

Unsupervised machine learning has identified ALS subtypes with distinct molecular and clinical features.

Current methods show promise in refining prognosis and enabling precision medicine for ALS.

Challenges remain in translating these findings into clinical practice due to methodological and data limitations.

Abstract

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterised by considerable heterogeneity in both its underlying biological mechanisms and clinical presentation. High-dimensional transcriptomic datasets offer an opportunity to characterise this variation at the molecular level; however, traditional statistical methods struggle with their scale and complexity.

Machine learning approaches can reduce dimensionality and uncover latent patterns, enabling the identification of molecular subtypes that may refine prognosis and support patient stratification. Recent transcriptomic studies employing unsupervised machine learning have identified ALS subtypes with distinct molecular and clinical characteristics. Redefining ALS into more homogeneous molecular and clinical subtypes could transform all areas of ALS research by supporting novel experimental designs and precision medicine approaches.

In this review, we summarise and critically assess these studies, discussing their findings, strengths, and limitations, and highlighting research gaps and challenges that must be addressed to enable their translation into biomedical and clinical practice.

The online version contains supplementary material available at 10.1186/s12916-026-04725-y.

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