ArticleScientific reports2018
Machine Learning Reveals Protein Signatures in CSF and Plasma Fluids of Clinical Value for ALS.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
31 citing papers in PubMed, 55 citations in OpenAlex.
- Using blood data for the differential diagnosis and prognosis of motor neuron diseases: a new dataset for machine learning applications.Scientific reports · 2021Trial
- Proteomic profile of CSF obtained at the time of diagnosis determines amyotrophic lateral sclerosis progression and survival: CXCL7 levels in disease prognosis and survival.Brain pathology (Zurich, Switzerland) · 2026Article
- A shared injury-homeostasis gradient in the human CSF proteome across neurological disease.Research square · 2026Article
- Skeletal Muscle Biomarkers of Amyotrophic Lateral Sclerosis: A Large-Scale, Multi-Cohort Proteomic Study.Annals of neurology · 2026Article
- The FindMNDBiomarker Program: Protein Changes in Motor Neuron Disease/Amyotrophic Lateral Sclerosis Postmortem Tissue and Biofluids.Annals of neurology · 2025Article
- Identification and validation of a tear fluid-derived protein biomarker signature in patients with amyotrophic lateral sclerosis.Acta neuropathologica communications · 2025Article
- Extracellular vesicles in TDP-43 proteinopathies: pathogenesis and biomarker potential.Molecular neurodegeneration · 2025Review
- Network analysis of the cerebrospinal fluid proteome reveals shared and unique differences between sporadic and familial forms of amyotrophic lateral sclerosis.Molecular neurodegeneration · 2025Article
- PERCEPT: Replacing binaryiScience · 2024Article
- Shotgun Proteomics Links Proteoglycan-4Biomolecules · 2024Article
- Article
- A beginner's guide into curated analyses of open access datasets for biomarker discovery in neurodegeneration.Scientific data · 2023Article
- SOMAscan Proteomics Identifies Novel Plasma Proteins in Amyotrophic Lateral Sclerosis Patients.International journal of molecular sciences · 2023Article
- Plasma and cerebrospinal fluid proteomic signatures of acutely sleep-deprived humans: an exploratory study.Sleep advances : a journal of the Sleep Research Society · 2023Article
- Plasma sterols and vitamin D are correlates and predictors of ozone-induced inflammation in the lung: A pilot study.PloS one · 2023Article
- Urinary biomarkers for amyotrophic lateral sclerosis: candidates, opportunities and considerations.Brain communications · 2023Review
- Article
- Proteomic-based evidence for adult neurogenesis in birds and mammals as indicated from cerebrospinal fluid.Neural regeneration research · 2022Review
- Biomarkers in Neurodegenerative Diseases: Proteomics Spotlight on ALS and Parkinson's Disease.International journal of molecular sciences · 2022Review
- Biomarker discovery and development for frontotemporal dementia and amyotrophic lateral sclerosis.Brain : a journal of neurology · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
We use shotgun proteomics to identify biomarkers of diagnostic and prognostic value in individuals diagnosed with amyotrophic lateral sclerosis. Matched cerebrospinal and plasma fluids were subjected to abundant protein depletion and analyzed by nano-flow liquid chromatography high resolution tandem mass spectrometry. Label free quantitation was used to identify differential proteins between individuals with ALS (n = 33) and healthy controls (n = 30) in both fluids. In CSF, 118 (p-value < 0.05) and 27 proteins (q-value < 0.05) were identified as significantly altered between ALS and controls. In plasma, 20 (p-value < 0.05) and 0 (q-value < 0.05) proteins were identified as significantly altered between ALS and controls. Proteins involved in complement activation, acute phase response and retinoid signaling pathways were significantly enriched in the CSF from ALS patients. Subsequently various machine learning methods were evaluated for disease classification using a repeated Monte Carlo cross-validation approach. A linear discriminant analysis model achieved a median area under the receiver operating characteristic curve of 0.94 with an interquartile range of 0.88-1.0. Three proteins composed a prognostic model (p = 5e-4) that explained 49% of the variation in the ALS-FRS scores. Finally we investigated the specificity of two promising proteins from our discovery data set, chitinase-3 like 1 protein and alpha-1-antichymotrypsin, using targeted proteomics in a separate set of CSF samples derived from individuals diagnosed with ALS (n = 11) and other neurological diseases (n = 15). These results demonstrate the potential of a panel of targeted proteins for objective measurements of clinical value in ALS.
Indexed as
Identifiers
What OpenQuestion holds
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.