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Using Machine Learning to Detect Theranostic Biomarkers Predicting Respiratory Treatment Response

Background: Theranostic approaches—the use of diagnostics for developing targeted therapies—are gaining popularity in the field of precision medicine. They are predominately used in cancer research, whereas there is little evidence of their use in respiratory medicine. This study aims to detect ther...

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Autores principales: Nikolaou, Vasilis, Massaro, Sebastiano, Fakhimi, Masoud, Garn, Wolfgang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9224718/
https://www.ncbi.nlm.nih.gov/pubmed/35743805
http://dx.doi.org/10.3390/life12060775
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author Nikolaou, Vasilis
Massaro, Sebastiano
Fakhimi, Masoud
Garn, Wolfgang
author_facet Nikolaou, Vasilis
Massaro, Sebastiano
Fakhimi, Masoud
Garn, Wolfgang
author_sort Nikolaou, Vasilis
collection PubMed
description Background: Theranostic approaches—the use of diagnostics for developing targeted therapies—are gaining popularity in the field of precision medicine. They are predominately used in cancer research, whereas there is little evidence of their use in respiratory medicine. This study aims to detect theranostic biomarkers associated with respiratory-treatment responses. This will advance theory and practice on the use of biomarkers in the diagnosis of respiratory diseases and contribute to developing targeted treatments. Methods: We performed a cross-sectional analysis on a sample of 13,102 adults from the UK household longitudinal study ‘Understanding Society’. We used recursive feature selection to identify 16 biomarkers associated with respiratory treatment responses. We then implemented several machine learning algorithms using the identified biomarkers as well as age, sex, body mass index, and lung function to predict treatment response. Results: Our analysis shows that subjects with increased levels of alkaline phosphatase, glycated haemoglobin, high-density lipoprotein cholesterol, c-reactive protein, triglycerides, hemoglobin, and Clauss fibrinogen are more likely to receive respiratory treatments, adjusting for age, sex, body mass index, and lung function. Conclusions: These findings offer a valuable blueprint on why and how the use of biomarkers as diagnostic tools can prove beneficial in guiding treatment management in respiratory diseases.
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spelling pubmed-92247182022-06-24 Using Machine Learning to Detect Theranostic Biomarkers Predicting Respiratory Treatment Response Nikolaou, Vasilis Massaro, Sebastiano Fakhimi, Masoud Garn, Wolfgang Life (Basel) Article Background: Theranostic approaches—the use of diagnostics for developing targeted therapies—are gaining popularity in the field of precision medicine. They are predominately used in cancer research, whereas there is little evidence of their use in respiratory medicine. This study aims to detect theranostic biomarkers associated with respiratory-treatment responses. This will advance theory and practice on the use of biomarkers in the diagnosis of respiratory diseases and contribute to developing targeted treatments. Methods: We performed a cross-sectional analysis on a sample of 13,102 adults from the UK household longitudinal study ‘Understanding Society’. We used recursive feature selection to identify 16 biomarkers associated with respiratory treatment responses. We then implemented several machine learning algorithms using the identified biomarkers as well as age, sex, body mass index, and lung function to predict treatment response. Results: Our analysis shows that subjects with increased levels of alkaline phosphatase, glycated haemoglobin, high-density lipoprotein cholesterol, c-reactive protein, triglycerides, hemoglobin, and Clauss fibrinogen are more likely to receive respiratory treatments, adjusting for age, sex, body mass index, and lung function. Conclusions: These findings offer a valuable blueprint on why and how the use of biomarkers as diagnostic tools can prove beneficial in guiding treatment management in respiratory diseases. MDPI 2022-05-24 /pmc/articles/PMC9224718/ /pubmed/35743805 http://dx.doi.org/10.3390/life12060775 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Nikolaou, Vasilis
Massaro, Sebastiano
Fakhimi, Masoud
Garn, Wolfgang
Using Machine Learning to Detect Theranostic Biomarkers Predicting Respiratory Treatment Response
title Using Machine Learning to Detect Theranostic Biomarkers Predicting Respiratory Treatment Response
title_full Using Machine Learning to Detect Theranostic Biomarkers Predicting Respiratory Treatment Response
title_fullStr Using Machine Learning to Detect Theranostic Biomarkers Predicting Respiratory Treatment Response
title_full_unstemmed Using Machine Learning to Detect Theranostic Biomarkers Predicting Respiratory Treatment Response
title_short Using Machine Learning to Detect Theranostic Biomarkers Predicting Respiratory Treatment Response
title_sort using machine learning to detect theranostic biomarkers predicting respiratory treatment response
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9224718/
https://www.ncbi.nlm.nih.gov/pubmed/35743805
http://dx.doi.org/10.3390/life12060775
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