생물정신의학 (Korean Journal of Biological Psychiatry)

  • 제30권1호
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  • Pages.24-30
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  • 2023
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  • 1225-8709(pISSN)
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  • 2005-7571(eISSN)
대한생물정신의학회 (The Korean Society of Biological Psychiatry)
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Identification of Combined Biomarker for Predicting Alzheimer's Disease Using Machine Learning

  • Ki-Yeol Kim (Oral Cancer Research Institute,Yonsei University College of Dentistry, Yonsei University)
  • 투고 : 2022.12.11
  • 심사 : 2023.01.30
  • 발행 : 2023.04.30
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초록

Objectives Alzheimer's disease (AD) is the most common form of dementia in older adults, damaging the brain and resulting in impaired memory, thinking, and behavior. The identification of differentially expressed genes and related pathways among affected brain regions can provide more information on the mechanisms of AD. The aim of our study was to identify differentially expressed genes associated with AD and combined biomarkers among them to improve AD risk prediction accuracy. Methods Machine learning methods were used to compare the performance of the identified combined biomarkers. In this study, three publicly available gene expression datasets from the hippocampal brain region were used. Results We detected 31 significant common genes from two different microarray datasets using the limma package. Some of them belonged to 11 biological pathways. Combined biomarkers were identified in two microarray datasets and were evaluated in a different dataset. The performance of the predictive models using the combined biomarkers was superior to those of models using a single gene. When two genes were combined, the most predictive gene set in the evaluation dataset was ATR and PRKCB when linear discriminant analysis was applied. Conclusions Combined biomarkers showed good performance in predicting the risk of AD. The constructed predictive nomogram using combined biomarkers could easily be used by clinicians to identify high-risk individuals so that more efficient trials could be designed to reduce the incidence of AD.

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과제정보

This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2021R1I1A1A01048175).

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