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Int. J. Mol. Sci. 2017, 18(6), 1217; doi:10.3390/ijms18061217

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

1
Institute of Clinical Pharmacology, Goethe-University, Theodor Stern Kai 7, Frankfurt am Main 60590, Germany
2
Fraunhofer Institute of Molecular Biology and Applied Ecology-Project Group Translational Medicine and Pharmacology (IME-TMP), Theodor-Stern-Kai 7, Frankfurt am Main 60590, Germany
3
DataBionics Research Group, University of Marburg, Hans-Meerwein-Strasse 6, Marburg 35032, Germany
4
Department of Neurology, Goethe-University Hospital, Schleusenweg 2-16, Frankfurt am Main 60528, Germany
*
Author to whom correspondence should be addressed.
Academic Editor: Giovanni Tarantino
Received: 25 April 2017 / Revised: 30 May 2017 / Accepted: 31 May 2017 / Published: 7 June 2017
(This article belongs to the Section Molecular Pathology, Diagnostics, and Therapeutics)
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Abstract

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. View Full-Text
Keywords: bioinformatics; data science; machine-learning; multiple sclerosis; prostanoids; ceramides bioinformatics; data science; machine-learning; multiple sclerosis; prostanoids; ceramides
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Lötsch, J.; Thrun, M.; Lerch, F.; Brunkhorst, R.; Schiffmann, S.; Thomas, D.; Tegder, I.; Geisslinger, G.; Ultsch, A. Machine-Learned Data Structures of Lipid Marker Serum Concentrations in Multiple Sclerosis Patients Differ from Those in Healthy Subjects. Int. J. Mol. Sci. 2017, 18, 1217.

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