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Lookup NU author(s): Professor Camille CarrollORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2023, Springer Nature Limited. As the population ages, neurodegenerative diseases are becoming more prevalent, making it crucial to comprehend the underlying disease mechanisms and identify biomarkers to allow for early diagnosis and effective screening for clinical trials. Thanks to advancements in gene expression profiling, it is now possible to search for disease biomarkers on an unprecedented scale.Here we applied a selection of five machine learning (ML) approaches to identify blood-based biomarkers for Alzheimer's (AD) and Parkinson's disease (PD) with the application of multiple feature selection methods. Based on ROC AUC performance, one optimal random forest (RF) model was discovered for AD with 159 gene markers (ROC-AUC = 0.886), while one optimal RF model was discovered for PD (ROC-AUC = 0.743). Additionally, in comparison to traditional ML approaches, deep learning approaches were applied to evaluate their potential applications in future works. We demonstrated that convolutional neural networks perform consistently well across both the Alzheimer's (ROC AUC = 0.810) and Parkinson's (ROC AUC = 0.715) datasets, suggesting its potential in gene expression biomarker detection with increased tuning of their architecture.
Author(s): Kelly J, Moyeed R, Carroll C, Luo S, Li X
Publication type: Article
Publication status: Published
Journal: Scientific Reports
Year: 2023
Volume: 13
Online publication date: 11/10/2023
Acceptance date: 30/09/2023
Date deposited: 30/01/2024
ISSN (electronic): 2045-2322
Publisher: Springer Nature
URL: https://doi.org/10.1038/s41598-023-43956-4
DOI: 10.1038/s41598-023-43956-4
PubMed id: 37821485
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