Unsupervised machine learning identifies distinct ALS molecular subtypes in post-mortem motor cortex and blood expression data

Unsupervised machine learning identifies distinct ALS molecular subtypes in post-mortem motor cortex and blood expression data

Abstract Amyotrophic lateral sclerosis (ALS) displays considerable clinical and genetic heterogeneity. Machine learning approaches have previously been utilised for patient stratification in ALS as they can disentangle complex disease landscapes. However, lack of independent validation in different...

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Bibliographic Details
Main Authors: Heather Marriott, Renata Kabiljo, Guy P Hunt, Ahmad Al Khleifat, Ashley Jones, Claire Troakes, Project MinE ALS Sequencing Consortium, TargetALS Sequencing Consortium, Abigail L Pfaff, John P Quinn, Sulev Koks, Richard J Dobson, Patrick Schwab, Ammar Al-Chalabi, Alfredo Iacoangeli
Format: Article
Language:English
Published: BMC 2023-12-01
Series:Acta Neuropathologica Communications
Subjects:
Amyotrophic lateral sclerosis
Unsupervised and supervised machine learning
Precision medicine
Transcriptomics
Patient stratification
Biomarkers
Online Access:https://doi.org/10.1186/s40478-023-01686-8

Internet

https://doi.org/10.1186/s40478-023-01686-8

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