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Circular-SWAT for deep learning based diagnostic classification of Alzheimer's disease: application to metabolome data.

Publication ,  Journal Article
Jo, T; Kim, J; Bice, P; Huynh, K; Wang, T; Arnold, M; Meikle, PJ; Giles, C; Kaddurah-Daouk, R; Saykin, AJ; Nho, K ...
Published in: EBioMedicine
November 2023

BACKGROUND: Deep learning has shown potential in various scientific domains but faces challenges when applied to complex, high-dimensional multi-omics data. Alzheimer's Disease (AD) is a neurodegenerative disorder that lacks targeted therapeutic options. This study introduces the Circular-Sliding Window Association Test (c-SWAT) to improve the classification accuracy in predicting AD using serum-based metabolomics data, specifically lipidomics. METHODS: The c-SWAT methodology builds upon the existing Sliding Window Association Test (SWAT) and utilizes a three-step approach: feature correlation analysis, feature selection, and classification. Data from 997 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) served as the basis for model training and validation. Feature correlations were analyzed using Weighted Gene Co-expression Network Analysis (WGCNA), and Convolutional Neural Networks (CNN) were employed for feature selection. Random Forest was used for the final classification. FINDINGS: The application of c-SWAT resulted in a classification accuracy of up to 80.8% and an AUC of 0.808 for distinguishing AD from cognitively normal older adults. This marks a 9.4% improvement in accuracy and a 0.169 increase in AUC compared to methods without c-SWAT. These results were statistically significant, with a p-value of 1.04 × 10ˆ-4. The approach also identified key lipids associated with AD, such as Cer(d16:1/22:0) and PI(37:6). INTERPRETATION: Our results indicate that c-SWAT is effective in improving classification accuracy and in identifying potential lipid biomarkers for AD. These identified lipids offer new avenues for understanding AD and warrant further investigation. FUNDING: The specific funding of this article is provided in the acknowledgements section.

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Published In

EBioMedicine

DOI

EISSN

2352-3964

Publication Date

November 2023

Volume

97

Start / End Page

104820

Location

Netherlands

Related Subject Headings

  • Neuroimaging
  • Metabolome
  • Magnetic Resonance Imaging
  • Lipids
  • Humans
  • Deep Learning
  • Cognitive Dysfunction
  • Alzheimer Disease
  • Aged
  • 4202 Epidemiology
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Jo, T., Kim, J., Bice, P., Huynh, K., Wang, T., Arnold, M., … Alzheimer’s Disease Neuroimaging Initiative (ADNI), . (2023). Circular-SWAT for deep learning based diagnostic classification of Alzheimer's disease: application to metabolome data. EBioMedicine, 97, 104820. https://doi.org/10.1016/j.ebiom.2023.104820
Jo, Taeho, Junpyo Kim, Paula Bice, Kevin Huynh, Tingting Wang, Matthias Arnold, Peter J. Meikle, et al. “Circular-SWAT for deep learning based diagnostic classification of Alzheimer's disease: application to metabolome data.EBioMedicine 97 (November 2023): 104820. https://doi.org/10.1016/j.ebiom.2023.104820.
Jo T, Kim J, Bice P, Huynh K, Wang T, Arnold M, et al. Circular-SWAT for deep learning based diagnostic classification of Alzheimer's disease: application to metabolome data. EBioMedicine. 2023 Nov;97:104820.
Jo, Taeho, et al. “Circular-SWAT for deep learning based diagnostic classification of Alzheimer's disease: application to metabolome data.EBioMedicine, vol. 97, Nov. 2023, p. 104820. Pubmed, doi:10.1016/j.ebiom.2023.104820.
Jo T, Kim J, Bice P, Huynh K, Wang T, Arnold M, Meikle PJ, Giles C, Kaddurah-Daouk R, Saykin AJ, Nho K, Alzheimer’s Disease Metabolomics Consortium (ADMC), Alzheimer’s Disease Neuroimaging Initiative (ADNI). Circular-SWAT for deep learning based diagnostic classification of Alzheimer's disease: application to metabolome data. EBioMedicine. 2023 Nov;97:104820.
Journal cover image

Published In

EBioMedicine

DOI

EISSN

2352-3964

Publication Date

November 2023

Volume

97

Start / End Page

104820

Location

Netherlands

Related Subject Headings

  • Neuroimaging
  • Metabolome
  • Magnetic Resonance Imaging
  • Lipids
  • Humans
  • Deep Learning
  • Cognitive Dysfunction
  • Alzheimer Disease
  • Aged
  • 4202 Epidemiology