ArticleInternational journal of molecular sciences2017
Machine-Learned Data Structures of Lipid Marker Serum Concentrations in Multiple Sclerosis Patients Differ from Those in Healthy Subjects.
Article in International journal of molecular sciences, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 31 citations in OpenAlex.
- The role of machine learning in developing non-magnetic resonance imaging based biomarkers for multiple sclerosis: a systematic review.BMC medical informatics and decision making · 2022Pooled it
- Augmenting small biomedical datasets using generative AI methods based on self-organizing neural networks.Briefings in bioinformatics · 2024Article
- Big data and artificial intelligence applied to blood and CSF fluid biomarkers in multiple sclerosis.Frontiers in immunology · 2024Review
- Multiple sclerosis diagnosis and phenotype identification by multivariate classification of in vivo frontal cortex metabolite profiles.Scientific reports · 2022Article
- Role of artificial intelligence in MS clinical practice.NeuroImage. Clinical · 2022Review
- Sapropterin (BH4) Aggravates Autoimmune Encephalomyelitis in Mice.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2021Observational
- Progranulin deficiency confers resistance to autoimmune encephalomyelitis in mice.Cellular & molecular immunology · 2020Article
- Metabolomic Biomarkers of Multiple Sclerosis: A Systematic Review.Frontiers in molecular biosciences · 2020Review
- A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases.NPJ digital medicine · 2020Review
- Alteration of Sphingolipids in Biofluids: Implications for Neurodegenerative Diseases.International journal of molecular sciences · 2019Review
- A Data Science-Based Analysis Points at Distinct Patterns of Lipid Mediator Plasma Concentrations in Patients With Dementia.Frontiers in psychiatry · 2019Article
- Machine-learning based lipid mediator serum concentration patterns allow identification of multiple sclerosis patients with high accuracy.Scientific reports · 2018Article
- Metabolic Dysfunction and Peroxisome Proliferator-Activated Receptors (PPAR) in Multiple Sclerosis.International journal of molecular sciences · 2018Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lipid metabolism has been suggested to be a major pathophysiological mechanism of multiple sclerosis (MS). With the increasing knowledge about lipid signaling, acquired data become increasingly complex making bioinformatics necessary in lipid research. We used unsupervised machine-learning to analyze lipid marker serum concentrations, pursuing the hypothesis that for the most relevant markers the emerging data structures will coincide with the diagnosis of MS. Machine learning was implemented as emergent self-organizing feature maps (ESOM) combined with the U*-matrix visualization technique. The data space consisted of serum concentrations of three main classes of lipid markers comprising eicosanoids (
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.