ArticleCell reports. Medicine2020
Inferring Multiple Sclerosis Stages from the Blood Transcriptome via Machine Learning.
Article in Cell reports. Medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
16 citing papers in PubMed, 34 citations in OpenAlex.
- Artificial intelligence in central nervous system autoimmune diseases: from clinical decision support to mechanistic discovery and translation.EMBO molecular medicine · 2026Review
- Impact of machine learning-driven analysis of blood transcriptomes in multiple sclerosis.Neural regeneration research · 2026Article
- Language Model Embedding Classifiers Enable Identification of Multiple Sclerosis-Associated BCRs and Repertoires.bioRxiv : the preprint server for biology · 2026Article
- Interpretable neural network for risk stratification and drug target discovery based on PBMC transcriptomes.iScience · 2026Article
- Semi-Supervised Learning for Predicting Multiple Sclerosis.Journal of personalized medicine · 2025Article
- Remyelinating Drugs at a Crossroad: How to Improve Clinical Efficacy and Drug Screenings.Cells · 2024Review
- Ultrastructural Characterization of PBMCs and Extracellular Vesicles in Multiple Sclerosis: A Pilot Study.International journal of molecular sciences · 2024Article
- Role of ferroptosis in neuroimmunity and neurodegeneration in multiple sclerosis revealed by multi-omics data.Journal of cellular and molecular medicine · 2024Article
- Big data and artificial intelligence applied to blood and CSF fluid biomarkers in multiple sclerosis.Frontiers in immunology · 2024Review
- Inhibition of pro-inflammatory signaling in human primary macrophages by enhancing arginase-2 via target site blockers.Molecular therapy. Nucleic acids · 2023Article
- Review
- AnMolecular genetics and metabolism reports · 2023Article
- Identification of Key Ferroptosis-Related Genes in the Peripheral Blood of Patients with Relapsing-Remitting Multiple Sclerosis and Its Diagnostic Value.International journal of molecular sciences · 2023Article
- Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities.Sensors (Basel, Switzerland) · 2022Review
- Role of artificial intelligence in MS clinical practice.NeuroImage. Clinical · 2022Review
- Contribution of Rare and Low-Frequency Variants to Multiple Sclerosis Susceptibility in the Italian Continental Population.Frontiers in genetics · 2021Article
Corrections and comments
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Authors and funding
11 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Peripheral blood mononuclear cells (PBMCs) bear specific dysregulations in genes and pathways at distinct stages of multiple sclerosis (MS) that may help with classifying MS and non-MS subjects, specifying the early stage of disease, or discriminating among MS courses. Here we describe an unbiased machine learning workflow to build MS stage-specific classifiers based on PBMC transcriptomics profiles from more than 300 individuals, including healthy subjects and patients with clinically isolated syndromes, relapsing-remitting MS, primary or secondary progressive MS, or other neurological disorders. The pipeline, designed to optimize and compare the performance of distinct machine learning algorithms in the training cohort, generates predictive models not influenced by demographic features, such as age and gender, and displays high accuracy in the independent validation cohort. Proper application of machine learning to transcriptional profiles of circulating blood cells may allow identification of disease state and stage in MS.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.