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Combination of Serum and Plasma Biomarkers Could Improve Prediction Performance for Alzheimer’s Disease

Alzheimer’s disease (AD) can be predicted either by serum or plasma biomarkers, and a combination may increase predictive power, but due to the high complexity of machine learning, it may also incur overfitting problems. In this paper, we investigated whether combining serum and plasma biomarkers wi...

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Autores principales: Zhang, Fan, Petersen, Melissa, Johnson, Leigh, Hall, James, O’Bryant, Sid E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9601501/
https://www.ncbi.nlm.nih.gov/pubmed/36292623
http://dx.doi.org/10.3390/genes13101738
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author Zhang, Fan
Petersen, Melissa
Johnson, Leigh
Hall, James
O’Bryant, Sid E.
author_facet Zhang, Fan
Petersen, Melissa
Johnson, Leigh
Hall, James
O’Bryant, Sid E.
author_sort Zhang, Fan
collection PubMed
description Alzheimer’s disease (AD) can be predicted either by serum or plasma biomarkers, and a combination may increase predictive power, but due to the high complexity of machine learning, it may also incur overfitting problems. In this paper, we investigated whether combining serum and plasma biomarkers with feature selection could improve prediction performance for AD. 150 D patients and 150 normal controls (NCs) were enrolled for a serum test, and 100 patients and 100 NCs were enrolled for the plasma test. Among these, 79 ADs and 65 NCs had serum and plasma samples in common. A 10 times repeated 5-fold cross-validation model and a feature selection method were used to overcome the overfitting problem when serum and plasma biomarkers were combined. First, we tested to see if simply adding serum and plasma biomarkers improved prediction performance but also caused overfitting. Then we employed a feature selection algorithm we developed to overcome the overfitting problem. Lastly, we tested the prediction performance in a 10 times repeated 5-fold cross validation model for training and testing sets. We found that the combined biomarkers improved AD prediction but also caused overfitting. A further feature selection based on the combination of serum and plasma biomarkers solved the problem and produced an even higher prediction performance than either serum or plasma biomarkers on their own. The combined feature-selected serum–plasma biomarkers may have critical implications for understanding the pathophysiology of AD and for developing preventative treatments.
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spelling pubmed-96015012022-10-27 Combination of Serum and Plasma Biomarkers Could Improve Prediction Performance for Alzheimer’s Disease Zhang, Fan Petersen, Melissa Johnson, Leigh Hall, James O’Bryant, Sid E. Genes (Basel) Article Alzheimer’s disease (AD) can be predicted either by serum or plasma biomarkers, and a combination may increase predictive power, but due to the high complexity of machine learning, it may also incur overfitting problems. In this paper, we investigated whether combining serum and plasma biomarkers with feature selection could improve prediction performance for AD. 150 D patients and 150 normal controls (NCs) were enrolled for a serum test, and 100 patients and 100 NCs were enrolled for the plasma test. Among these, 79 ADs and 65 NCs had serum and plasma samples in common. A 10 times repeated 5-fold cross-validation model and a feature selection method were used to overcome the overfitting problem when serum and plasma biomarkers were combined. First, we tested to see if simply adding serum and plasma biomarkers improved prediction performance but also caused overfitting. Then we employed a feature selection algorithm we developed to overcome the overfitting problem. Lastly, we tested the prediction performance in a 10 times repeated 5-fold cross validation model for training and testing sets. We found that the combined biomarkers improved AD prediction but also caused overfitting. A further feature selection based on the combination of serum and plasma biomarkers solved the problem and produced an even higher prediction performance than either serum or plasma biomarkers on their own. The combined feature-selected serum–plasma biomarkers may have critical implications for understanding the pathophysiology of AD and for developing preventative treatments. MDPI 2022-09-27 /pmc/articles/PMC9601501/ /pubmed/36292623 http://dx.doi.org/10.3390/genes13101738 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
Zhang, Fan
Petersen, Melissa
Johnson, Leigh
Hall, James
O’Bryant, Sid E.
Combination of Serum and Plasma Biomarkers Could Improve Prediction Performance for Alzheimer’s Disease
title Combination of Serum and Plasma Biomarkers Could Improve Prediction Performance for Alzheimer’s Disease
title_full Combination of Serum and Plasma Biomarkers Could Improve Prediction Performance for Alzheimer’s Disease
title_fullStr Combination of Serum and Plasma Biomarkers Could Improve Prediction Performance for Alzheimer’s Disease
title_full_unstemmed Combination of Serum and Plasma Biomarkers Could Improve Prediction Performance for Alzheimer’s Disease
title_short Combination of Serum and Plasma Biomarkers Could Improve Prediction Performance for Alzheimer’s Disease
title_sort combination of serum and plasma biomarkers could improve prediction performance for alzheimer’s disease
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9601501/
https://www.ncbi.nlm.nih.gov/pubmed/36292623
http://dx.doi.org/10.3390/genes13101738
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