Comparison of machine-learning methodologies for accurate diagnosis of sepsis using microarray gene expression data.

We investigate the feasibility of molecular-level sample classification of sepsis using microarray gene expression data merged by in silico meta-analysis. Publicly available data series were extracted from NCBI Gene Expression Omnibus and EMBL-EBI ArrayExpress to create a comprehensive meta-analysis...

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Autores principales:	Dominik Schaack, Markus A Weigand, Florian Uhle
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/229cd1e057ca4c62ac611b4e7b29f9ea
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spelling	oai:doaj.org-article:229cd1e057ca4c62ac611b4e7b29f9ea2021-11-25T06:19:10ZComparison of machine-learning methodologies for accurate diagnosis of sepsis using microarray gene expression data.1932-620310.1371/journal.pone.0251800https://doaj.org/article/229cd1e057ca4c62ac611b4e7b29f9ea2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0251800https://doaj.org/toc/1932-6203We investigate the feasibility of molecular-level sample classification of sepsis using microarray gene expression data merged by in silico meta-analysis. Publicly available data series were extracted from NCBI Gene Expression Omnibus and EMBL-EBI ArrayExpress to create a comprehensive meta-analysis microarray expression set (meta-expression set). Measurements had to be obtained via microarray-technique from whole blood samples of adult or pediatric patients with sepsis diagnosed based on international consensus definition immediately after admission to the intensive care unit. We aggregate trauma patients, systemic inflammatory response syndrome (SIRS) patients, and healthy controls in a non-septic entity. Differential expression (DE) analysis is compared with machine-learning-based solutions like decision tree (DT), random forest (RF), support vector machine (SVM), and deep-learning neural networks (DNNs). We evaluated classifier training and discrimination performance in 100 independent iterations. To test diagnostic resilience, we gradually degraded expression data in multiple levels. Clustering of expression values based on DE genes results in partial identification of sepsis samples. In contrast, RF, SVM, and DNN provide excellent diagnostic performance measured in terms of accuracy and area under the curve (>0.96 and >0.99, respectively). We prove DNNs as the most resilient methodology, virtually unaffected by targeted removal of DE genes. By surpassing most other published solutions, the presented approach substantially augments current diagnostic capability in intensive care medicine.Dominik SchaackMarkus A WeigandFlorian UhlePublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 5, p e0251800 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Dominik Schaack Markus A Weigand Florian Uhle Comparison of machine-learning methodologies for accurate diagnosis of sepsis using microarray gene expression data.
description	We investigate the feasibility of molecular-level sample classification of sepsis using microarray gene expression data merged by in silico meta-analysis. Publicly available data series were extracted from NCBI Gene Expression Omnibus and EMBL-EBI ArrayExpress to create a comprehensive meta-analysis microarray expression set (meta-expression set). Measurements had to be obtained via microarray-technique from whole blood samples of adult or pediatric patients with sepsis diagnosed based on international consensus definition immediately after admission to the intensive care unit. We aggregate trauma patients, systemic inflammatory response syndrome (SIRS) patients, and healthy controls in a non-septic entity. Differential expression (DE) analysis is compared with machine-learning-based solutions like decision tree (DT), random forest (RF), support vector machine (SVM), and deep-learning neural networks (DNNs). We evaluated classifier training and discrimination performance in 100 independent iterations. To test diagnostic resilience, we gradually degraded expression data in multiple levels. Clustering of expression values based on DE genes results in partial identification of sepsis samples. In contrast, RF, SVM, and DNN provide excellent diagnostic performance measured in terms of accuracy and area under the curve (>0.96 and >0.99, respectively). We prove DNNs as the most resilient methodology, virtually unaffected by targeted removal of DE genes. By surpassing most other published solutions, the presented approach substantially augments current diagnostic capability in intensive care medicine.
format	article
author	Dominik Schaack Markus A Weigand Florian Uhle
author_facet	Dominik Schaack Markus A Weigand Florian Uhle
author_sort	Dominik Schaack
title	Comparison of machine-learning methodologies for accurate diagnosis of sepsis using microarray gene expression data.
title_short	Comparison of machine-learning methodologies for accurate diagnosis of sepsis using microarray gene expression data.
title_full	Comparison of machine-learning methodologies for accurate diagnosis of sepsis using microarray gene expression data.
title_fullStr	Comparison of machine-learning methodologies for accurate diagnosis of sepsis using microarray gene expression data.
title_full_unstemmed	Comparison of machine-learning methodologies for accurate diagnosis of sepsis using microarray gene expression data.
title_sort	comparison of machine-learning methodologies for accurate diagnosis of sepsis using microarray gene expression data.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/229cd1e057ca4c62ac611b4e7b29f9ea
work_keys_str_mv	AT dominikschaack comparisonofmachinelearningmethodologiesforaccuratediagnosisofsepsisusingmicroarraygeneexpressiondata AT markusaweigand comparisonofmachinelearningmethodologiesforaccuratediagnosisofsepsisusingmicroarraygeneexpressiondata AT florianuhle comparisonofmachinelearningmethodologiesforaccuratediagnosisofsepsisusingmicroarraygeneexpressiondata
_version_	1718413911289495552

Comparison of machine-learning methodologies for accurate diagnosis of sepsis using microarray gene expression data.

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