ArticleEuropean journal of human genetics : EJHG2025
Machine learning predicts distinct biotypes of amyotrophic lateral sclerosis.
Article in European journal of human genetics : EJHG, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- An in silico protocol for predicting genetic biomarkers in rare diseases: a case study in sporadic amyotrophic lateral sclerosis.Frontiers in genetics · 2026Article
- Uncertainty, ethics, and progress in genomic medicine.European journal of human genetics : EJHG · 2025Article
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Authors and funding
3 authors.
Funding
Abstract
Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease that is universally fatal and has no cure. Heterogeneity of clinical presentation, disease onset, and proposed pathological mechanisms are key reasons why developing impactful therapies for ALS has been challenging. Here we analyzed data from two postmortem cohorts: one with bulk transcriptomes from 297 ALS patients and a separate cohort of single cell transcriptomes from 23 ALS patients. Using unsupervised machine learning, we found three groups of ALS patients characterized by synaptic dysfunction (34%), neuronal regeneration (47%), and neuronal degeneration (19%). Each of these ALS subtypes had unique patterns of transcriptional dysregulation that could represent novel therapeutic targets. We then developed a supervised machine learning model that was about 80% accurate at predicting ALS subtype based on patient demographic and clinical data. Together, we established three biologically distinct subtypes of ALS that can be predicted by clinical and demographic data.
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