Cargando…

Machine-Learned Data Structures of Lipid Marker Serum Concentrations in Multiple Sclerosis Patients Differ from Those in Healthy Subjects

Lipid signaling 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...

Descripción completa

Detalles Bibliográficos
Autores principales: Lötsch, Jörn, Thrun, Michael, Lerch, Florian, Brunkhorst, Robert, Schiffmann, Susanne, Thomas, Dominique, Tegder, Irmgard, Geisslinger, Gerd, Ultsch, Alfred
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5486040/
https://www.ncbi.nlm.nih.gov/pubmed/28590455
http://dx.doi.org/10.3390/ijms18061217
_version_ 1783246186525753344
author Lötsch, Jörn
Thrun, Michael
Lerch, Florian
Brunkhorst, Robert
Schiffmann, Susanne
Thomas, Dominique
Tegder, Irmgard
Geisslinger, Gerd
Ultsch, Alfred
author_facet Lötsch, Jörn
Thrun, Michael
Lerch, Florian
Brunkhorst, Robert
Schiffmann, Susanne
Thomas, Dominique
Tegder, Irmgard
Geisslinger, Gerd
Ultsch, Alfred
author_sort Lötsch, Jörn
collection PubMed
description Lipid signaling 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 (d = 11 markers), ceramides (d = 10), and lyosophosphatidic acids (d = 6). They were analyzed in cohorts of MS patients (n = 102) and healthy subjects (n = 301). Clear data structures in the high-dimensional data space were observed in eicosanoid and ceramides serum concentrations whereas no clear structure could be found in lysophosphatidic acid concentrations. With ceramide concentrations, the structures that had emerged from unsupervised machine-learning almost completely overlapped with the known grouping of MS patients versus healthy subjects. This was only partly provided by eicosanoid serum concentrations. Thus, unsupervised machine-learning identified distinct data structures of bioactive lipid serum concentrations. These structures could be superimposed with the known grouping of MS patients versus healthy subjects, which was almost completely possible with ceramides. Therefore, based on the present analysis, ceramides are first-line candidates for further exploration as drug-gable targets or biomarkers in MS.
format Online
Article
Text
id pubmed-5486040
institution National Center for Biotechnology Information
language English
publishDate 2017
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-54860402017-06-29 Machine-Learned Data Structures of Lipid Marker Serum Concentrations in Multiple Sclerosis Patients Differ from Those in Healthy Subjects Lötsch, Jörn Thrun, Michael Lerch, Florian Brunkhorst, Robert Schiffmann, Susanne Thomas, Dominique Tegder, Irmgard Geisslinger, Gerd Ultsch, Alfred Int J Mol Sci Article Lipid signaling 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 (d = 11 markers), ceramides (d = 10), and lyosophosphatidic acids (d = 6). They were analyzed in cohorts of MS patients (n = 102) and healthy subjects (n = 301). Clear data structures in the high-dimensional data space were observed in eicosanoid and ceramides serum concentrations whereas no clear structure could be found in lysophosphatidic acid concentrations. With ceramide concentrations, the structures that had emerged from unsupervised machine-learning almost completely overlapped with the known grouping of MS patients versus healthy subjects. This was only partly provided by eicosanoid serum concentrations. Thus, unsupervised machine-learning identified distinct data structures of bioactive lipid serum concentrations. These structures could be superimposed with the known grouping of MS patients versus healthy subjects, which was almost completely possible with ceramides. Therefore, based on the present analysis, ceramides are first-line candidates for further exploration as drug-gable targets or biomarkers in MS. MDPI 2017-06-07 /pmc/articles/PMC5486040/ /pubmed/28590455 http://dx.doi.org/10.3390/ijms18061217 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Lötsch, Jörn
Thrun, Michael
Lerch, Florian
Brunkhorst, Robert
Schiffmann, Susanne
Thomas, Dominique
Tegder, Irmgard
Geisslinger, Gerd
Ultsch, Alfred
Machine-Learned Data Structures of Lipid Marker Serum Concentrations in Multiple Sclerosis Patients Differ from Those in Healthy Subjects
title Machine-Learned Data Structures of Lipid Marker Serum Concentrations in Multiple Sclerosis Patients Differ from Those in Healthy Subjects
title_full Machine-Learned Data Structures of Lipid Marker Serum Concentrations in Multiple Sclerosis Patients Differ from Those in Healthy Subjects
title_fullStr Machine-Learned Data Structures of Lipid Marker Serum Concentrations in Multiple Sclerosis Patients Differ from Those in Healthy Subjects
title_full_unstemmed Machine-Learned Data Structures of Lipid Marker Serum Concentrations in Multiple Sclerosis Patients Differ from Those in Healthy Subjects
title_short Machine-Learned Data Structures of Lipid Marker Serum Concentrations in Multiple Sclerosis Patients Differ from Those in Healthy Subjects
title_sort machine-learned data structures of lipid marker serum concentrations in multiple sclerosis patients differ from those in healthy subjects
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5486040/
https://www.ncbi.nlm.nih.gov/pubmed/28590455
http://dx.doi.org/10.3390/ijms18061217
work_keys_str_mv AT lotschjorn machinelearneddatastructuresoflipidmarkerserumconcentrationsinmultiplesclerosispatientsdifferfromthoseinhealthysubjects
AT thrunmichael machinelearneddatastructuresoflipidmarkerserumconcentrationsinmultiplesclerosispatientsdifferfromthoseinhealthysubjects
AT lerchflorian machinelearneddatastructuresoflipidmarkerserumconcentrationsinmultiplesclerosispatientsdifferfromthoseinhealthysubjects
AT brunkhorstrobert machinelearneddatastructuresoflipidmarkerserumconcentrationsinmultiplesclerosispatientsdifferfromthoseinhealthysubjects
AT schiffmannsusanne machinelearneddatastructuresoflipidmarkerserumconcentrationsinmultiplesclerosispatientsdifferfromthoseinhealthysubjects
AT thomasdominique machinelearneddatastructuresoflipidmarkerserumconcentrationsinmultiplesclerosispatientsdifferfromthoseinhealthysubjects
AT tegderirmgard machinelearneddatastructuresoflipidmarkerserumconcentrationsinmultiplesclerosispatientsdifferfromthoseinhealthysubjects
AT geisslingergerd machinelearneddatastructuresoflipidmarkerserumconcentrationsinmultiplesclerosispatientsdifferfromthoseinhealthysubjects
AT ultschalfred machinelearneddatastructuresoflipidmarkerserumconcentrationsinmultiplesclerosispatientsdifferfromthoseinhealthysubjects