ArticleScientific reports2018
Machine-learning based lipid mediator serum concentration patterns allow identification of multiple sclerosis patients with high accuracy.
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 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- 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
- Recent Advances in Metabolomics and Lipidomics Studies in Human and Animal Models of Multiple Sclerosis.Metabolites · 2024Review
- Comparative targeted lipidomics between serum and cerebrospinal fluid of multiple sclerosis patients shows sex and age-specific differences of endocannabinoids and glucocorticoids.Acta neuropathologica communications · 2024Article
- Dysregulated lipid metabolism networks modulate T-cell function in people with relapsing-remitting multiple sclerosis.Clinical and experimental immunology · 2024Article
- Diet Supplemented with Special Formula Milk Powder Promotes the Growth of the Brain in Rats.Nutrients · 2024Article
- Blood metabolomic and transcriptomic signatures stratify patient subgroups in multiple sclerosis according to disease severity.iScience · 2024Article
- Plasma Lipidomic Profiling Using Mass Spectrometry for Multiple Sclerosis Diagnosis and Disease Activity Stratification (LipidMS).International journal of molecular sciences · 2024Article
- Big data and artificial intelligence applied to blood and CSF fluid biomarkers in multiple sclerosis.Frontiers in immunology · 2024Review
- Recursive computed ABC (cABC) analysis as a precise method for reducing machine learning based feature sets to their minimum informative size.Scientific reports · 2023Article
- ATR-FTIR spectroscopy of plasma supported by multivariate analysis discriminates multiple sclerosis disease.Scientific reports · 2023Article
- Endocannabinoid levels in peripheral blood mononuclear cells of multiple sclerosis patients treated with dimethyl fumarate.Scientific reports · 2022Article
- Robust Classification Using Posterior Probability Threshold Computation Followed by Voronoi Cell Based Class Assignment Circumventing Pitfalls of Bayesian Analysis of Biomedical Data.International journal of molecular sciences · 2022Article
- Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities.Sensors (Basel, Switzerland) · 2022Review
- Machine Learning and Pathway Analysis-Based Discovery of Metabolomic Markers Relating to Chronic Pain Phenotypes.International journal of molecular sciences · 2022Article
- Machine-Learning Analysis of Serum Proteomics in Neuropathic Pain after Nerve Injury in Breast Cancer Surgery Points at Chemokine Signaling via SIRT2 Regulation.International journal of molecular sciences · 2022Article
- Role of artificial intelligence in MS clinical practice.NeuroImage. Clinical · 2022Review
- Article
- Sphingolipid and Endocannabinoid Profiles in Adult Attention Deficit Hyperactivity Disorder.Biomedicines · 2021Article
- Sapropterin (BH4) Aggravates Autoimmune Encephalomyelitis in Mice.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2021Observational
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Based on increasing evidence suggesting that MS pathology involves alterations in bioactive lipid metabolism, the present analysis was aimed at generating a complex serum lipid-biomarker. Using unsupervised machine-learning, implemented as emergent self-organizing maps of neuronal networks, swarm intelligence and Minimum Curvilinear Embedding, a cluster structure was found in the input data space comprising serum concentrations of d = 43 different lipid-markers of various classes. The structure coincided largely with the clinical diagnosis, indicating that the data provide a basis for the creation of a biomarker (classifier). This was subsequently assessed using supervised machine-learning, implemented as random forests and computed ABC analysis-based feature selection. Bayesian statistics-based biomarker creation was used to map the diagnostic classes of either MS patients (n = 102) or healthy subjects (n = 301). Eight lipid-markers passed the feature selection and comprised GluCerC16, LPA20:4, HETE15S, LacCerC24:1, C16Sphinganine, biopterin and the endocannabinoids PEA and OEA. A complex classifier or biomarker was developed that predicted MS at a sensitivity, specificity and accuracy of approximately 95% in training and test data sets, respectively. The present successful application of serum lipid marker concentrations to MS data is encouraging for further efforts to establish an MS biomarker based on serum lipidomics.
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