Artificial intelligence and the analysis of multi-platform metabolomics data for the detection of intrauterine growth restriction.

<h4>Objective</h4>To interrogate the pathogenesis of intrauterine growth restriction (IUGR) and apply Artificial Intelligence (AI) techniques to multi-platform i.e. nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) based metabolomic analysis for the prediction of I...

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Main Authors: Ray Oliver Bahado-Singh, Ali Yilmaz, Halil Bisgin, Onur Turkoglu, Praveen Kumar, Eric Sherman, Andrew Mrazik, Anthony Odibo, Stewart F Graham
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2019-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0214121
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spelling doaj-1fb464540d974cc095ec43e3907770b72021-03-04T10:33:08ZengPublic Library of Science (PLoS)PLoS ONE1932-62032019-01-01144e021412110.1371/journal.pone.0214121Artificial intelligence and the analysis of multi-platform metabolomics data for the detection of intrauterine growth restriction.Ray Oliver Bahado-SinghAli YilmazHalil BisginOnur TurkogluPraveen KumarEric ShermanAndrew MrazikAnthony OdiboStewart F Graham<h4>Objective</h4>To interrogate the pathogenesis of intrauterine growth restriction (IUGR) and apply Artificial Intelligence (AI) techniques to multi-platform i.e. nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) based metabolomic analysis for the prediction of IUGR.<h4>Materials and methods</h4>MS and NMR based metabolomic analysis were performed on cord blood serum from 40 IUGR (birth weight < 10th percentile) cases and 40 controls. Three variable selection algorithms namely: Correlation-based feature selection (CFS), Partial least squares regression (PLS) and Learning Vector Quantization (LVQ) were tested for their diagnostic performance. For each selected set of metabolites and the panel consists of metabolites common in three selection algorithms so-called overlapping set (OL), support vector machine (SVM) models were developed for which parameter selection was performed busing 10-fold cross validations. Area under the receiver operating characteristics curve (AUC), sensitivity and specificity values were calculated for IUGR diagnosis. Metabolite set enrichment analysis (MSEA) was performed to identify which metabolic pathways were perturbed as a direct result of IUGR in cord blood serum.<h4>Results</h4>All selected metabolites and their overlapping set achieved statistically significant accuracies in the range of 0.78-0.82 for their optimized SVM models. The model utilizing all metabolites in the dataset had an AUC = 0.91 with a sensitivity of 0.83 and specificity equal to 0.80. CFS and OL (Creatinine, C2, C4, lysoPC.a.C16.1, lysoPC.a.C20.3, lysoPC.a.C28.1, PC.aa.C24.0) showed the highest performance with sensitivity (0.87) and specificity (0.87), respectively. MSEA revealed significantly altered metabolic pathways in IUGR cases. Dysregulated pathways include: beta oxidation of very long fatty acids, oxidation of branched chain fatty acids, phospholipid biosynthesis, lysine degradation, urea cycle and fatty acid metabolism.<h4>Conclusion</h4>A systematically selected panel of metabolites was shown to accurately detect IUGR in newborn cord blood serum. Significant disturbance of hepatic function and energy generating pathways were found in IUGR cases.https://doi.org/10.1371/journal.pone.0214121
collection DOAJ
language English
format Article
sources DOAJ
author Ray Oliver Bahado-Singh
Ali Yilmaz
Halil Bisgin
Onur Turkoglu
Praveen Kumar
Eric Sherman
Andrew Mrazik
Anthony Odibo
Stewart F Graham
spellingShingle Ray Oliver Bahado-Singh
Ali Yilmaz
Halil Bisgin
Onur Turkoglu
Praveen Kumar
Eric Sherman
Andrew Mrazik
Anthony Odibo
Stewart F Graham
Artificial intelligence and the analysis of multi-platform metabolomics data for the detection of intrauterine growth restriction.
PLoS ONE
author_facet Ray Oliver Bahado-Singh
Ali Yilmaz
Halil Bisgin
Onur Turkoglu
Praveen Kumar
Eric Sherman
Andrew Mrazik
Anthony Odibo
Stewart F Graham
author_sort Ray Oliver Bahado-Singh
title Artificial intelligence and the analysis of multi-platform metabolomics data for the detection of intrauterine growth restriction.
title_short Artificial intelligence and the analysis of multi-platform metabolomics data for the detection of intrauterine growth restriction.
title_full Artificial intelligence and the analysis of multi-platform metabolomics data for the detection of intrauterine growth restriction.
title_fullStr Artificial intelligence and the analysis of multi-platform metabolomics data for the detection of intrauterine growth restriction.
title_full_unstemmed Artificial intelligence and the analysis of multi-platform metabolomics data for the detection of intrauterine growth restriction.
title_sort artificial intelligence and the analysis of multi-platform metabolomics data for the detection of intrauterine growth restriction.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2019-01-01
description <h4>Objective</h4>To interrogate the pathogenesis of intrauterine growth restriction (IUGR) and apply Artificial Intelligence (AI) techniques to multi-platform i.e. nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) based metabolomic analysis for the prediction of IUGR.<h4>Materials and methods</h4>MS and NMR based metabolomic analysis were performed on cord blood serum from 40 IUGR (birth weight < 10th percentile) cases and 40 controls. Three variable selection algorithms namely: Correlation-based feature selection (CFS), Partial least squares regression (PLS) and Learning Vector Quantization (LVQ) were tested for their diagnostic performance. For each selected set of metabolites and the panel consists of metabolites common in three selection algorithms so-called overlapping set (OL), support vector machine (SVM) models were developed for which parameter selection was performed busing 10-fold cross validations. Area under the receiver operating characteristics curve (AUC), sensitivity and specificity values were calculated for IUGR diagnosis. Metabolite set enrichment analysis (MSEA) was performed to identify which metabolic pathways were perturbed as a direct result of IUGR in cord blood serum.<h4>Results</h4>All selected metabolites and their overlapping set achieved statistically significant accuracies in the range of 0.78-0.82 for their optimized SVM models. The model utilizing all metabolites in the dataset had an AUC = 0.91 with a sensitivity of 0.83 and specificity equal to 0.80. CFS and OL (Creatinine, C2, C4, lysoPC.a.C16.1, lysoPC.a.C20.3, lysoPC.a.C28.1, PC.aa.C24.0) showed the highest performance with sensitivity (0.87) and specificity (0.87), respectively. MSEA revealed significantly altered metabolic pathways in IUGR cases. Dysregulated pathways include: beta oxidation of very long fatty acids, oxidation of branched chain fatty acids, phospholipid biosynthesis, lysine degradation, urea cycle and fatty acid metabolism.<h4>Conclusion</h4>A systematically selected panel of metabolites was shown to accurately detect IUGR in newborn cord blood serum. Significant disturbance of hepatic function and energy generating pathways were found in IUGR cases.
url https://doi.org/10.1371/journal.pone.0214121
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