Cargando…
The application of artificial intelligence methods to gene expression data for differentiation of uncomplicated and complicated appendicitis in children and adolescents - a proof of concept study –
BACKGROUND: Genome wide gene expression analysis has revealed hints for independent immunological pathways underlying the pathophysiologies of phlegmonous (PA) and gangrenous appendicitis (GA). Methods of artificial intelligence (AI) have successfully been applied to routine laboratory and sonograph...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8186230/ https://www.ncbi.nlm.nih.gov/pubmed/34103023 http://dx.doi.org/10.1186/s12887-021-02735-8 |
_version_ | 1783704919209934848 |
---|---|
author | Reismann, Josephine Kiss, Natalie Reismann, Marc |
author_facet | Reismann, Josephine Kiss, Natalie Reismann, Marc |
author_sort | Reismann, Josephine |
collection | PubMed |
description | BACKGROUND: Genome wide gene expression analysis has revealed hints for independent immunological pathways underlying the pathophysiologies of phlegmonous (PA) and gangrenous appendicitis (GA). Methods of artificial intelligence (AI) have successfully been applied to routine laboratory and sonographic parameters for differentiation of the inflammatory manifestations. In this study we aimed to apply AI methods to gene expression data to provide evidence for feasibility. METHODS: Modern algorithms from AI were applied to 56.666 gene expression data sets from 13 patients with PA and 16 with GA aged 7–17 years by using resampling methods (bootstrap). Performance with respect to sensitivities and specificities where investigated with receiver operating characteristic (ROC) analysis. RESULTS: Within the experimental setting a best performing discriminatory biomarker signature consisting of a set of 4 genes could be defined: ERGIC and golgi 3, regulator of G-protein signaling 2, Rho GTPase activating protein 33, and Golgi Reassembly Stacking Protein 2. ROC analysis showed a mean area under the curve of 84%. CONCLUSIONS: Gene expression based application of AI methods is feasible and represents a promising approach for future discriminatory diagnostics in children with acute appendicitis. |
format | Online Article Text |
id | pubmed-8186230 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-81862302021-06-10 The application of artificial intelligence methods to gene expression data for differentiation of uncomplicated and complicated appendicitis in children and adolescents - a proof of concept study – Reismann, Josephine Kiss, Natalie Reismann, Marc BMC Pediatr Research Article BACKGROUND: Genome wide gene expression analysis has revealed hints for independent immunological pathways underlying the pathophysiologies of phlegmonous (PA) and gangrenous appendicitis (GA). Methods of artificial intelligence (AI) have successfully been applied to routine laboratory and sonographic parameters for differentiation of the inflammatory manifestations. In this study we aimed to apply AI methods to gene expression data to provide evidence for feasibility. METHODS: Modern algorithms from AI were applied to 56.666 gene expression data sets from 13 patients with PA and 16 with GA aged 7–17 years by using resampling methods (bootstrap). Performance with respect to sensitivities and specificities where investigated with receiver operating characteristic (ROC) analysis. RESULTS: Within the experimental setting a best performing discriminatory biomarker signature consisting of a set of 4 genes could be defined: ERGIC and golgi 3, regulator of G-protein signaling 2, Rho GTPase activating protein 33, and Golgi Reassembly Stacking Protein 2. ROC analysis showed a mean area under the curve of 84%. CONCLUSIONS: Gene expression based application of AI methods is feasible and represents a promising approach for future discriminatory diagnostics in children with acute appendicitis. BioMed Central 2021-06-08 /pmc/articles/PMC8186230/ /pubmed/34103023 http://dx.doi.org/10.1186/s12887-021-02735-8 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Reismann, Josephine Kiss, Natalie Reismann, Marc The application of artificial intelligence methods to gene expression data for differentiation of uncomplicated and complicated appendicitis in children and adolescents - a proof of concept study – |
title | The application of artificial intelligence methods to gene expression data for differentiation of uncomplicated and complicated appendicitis in children and adolescents - a proof of concept study – |
title_full | The application of artificial intelligence methods to gene expression data for differentiation of uncomplicated and complicated appendicitis in children and adolescents - a proof of concept study – |
title_fullStr | The application of artificial intelligence methods to gene expression data for differentiation of uncomplicated and complicated appendicitis in children and adolescents - a proof of concept study – |
title_full_unstemmed | The application of artificial intelligence methods to gene expression data for differentiation of uncomplicated and complicated appendicitis in children and adolescents - a proof of concept study – |
title_short | The application of artificial intelligence methods to gene expression data for differentiation of uncomplicated and complicated appendicitis in children and adolescents - a proof of concept study – |
title_sort | application of artificial intelligence methods to gene expression data for differentiation of uncomplicated and complicated appendicitis in children and adolescents - a proof of concept study – |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8186230/ https://www.ncbi.nlm.nih.gov/pubmed/34103023 http://dx.doi.org/10.1186/s12887-021-02735-8 |
work_keys_str_mv | AT reismannjosephine theapplicationofartificialintelligencemethodstogeneexpressiondatafordifferentiationofuncomplicatedandcomplicatedappendicitisinchildrenandadolescentsaproofofconceptstudy AT kissnatalie theapplicationofartificialintelligencemethodstogeneexpressiondatafordifferentiationofuncomplicatedandcomplicatedappendicitisinchildrenandadolescentsaproofofconceptstudy AT reismannmarc theapplicationofartificialintelligencemethodstogeneexpressiondatafordifferentiationofuncomplicatedandcomplicatedappendicitisinchildrenandadolescentsaproofofconceptstudy AT reismannjosephine applicationofartificialintelligencemethodstogeneexpressiondatafordifferentiationofuncomplicatedandcomplicatedappendicitisinchildrenandadolescentsaproofofconceptstudy AT kissnatalie applicationofartificialintelligencemethodstogeneexpressiondatafordifferentiationofuncomplicatedandcomplicatedappendicitisinchildrenandadolescentsaproofofconceptstudy AT reismannmarc applicationofartificialintelligencemethodstogeneexpressiondatafordifferentiationofuncomplicatedandcomplicatedappendicitisinchildrenandadolescentsaproofofconceptstudy |