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

dc.contributor.authorJo, Taeho
dc.contributor.authorKim, Junpyo
dc.contributor.authorBice, Paula
dc.contributor.authorHuynh, Kevin
dc.contributor.authorWang, Tingting
dc.contributor.authorArnold, Matthias
dc.contributor.authorMeikle, Peter J.
dc.contributor.authorGiles, Corey
dc.contributor.authorKaddurah-Daouk, Rima
dc.contributor.authorSaykin, Andrew J.
dc.contributor.authorNho, Kwangsik
dc.contributor.authorAlzheimer’s Disease Metabolomics Consortium (ADMC)
dc.contributor.authorAlzheimer’s Disease Neuroimaging Initiative (ADNI)
dc.contributor.departmentRadiology and Imaging Sciences, School of Medicine
dc.date.accessioned2024-03-22T09:03:31Z
dc.date.available2024-03-22T09:03:31Z
dc.date.issued2023
dc.description.abstractBackground: 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.
dc.eprint.versionFinal published version
dc.identifier.citationJo T, Kim J, Bice P, et al. Circular-SWAT for deep learning based diagnostic classification of Alzheimer's disease: application to metabolome data. EBioMedicine. 2023;97:104820. doi:10.1016/j.ebiom.2023.104820
dc.identifier.urihttps://hdl.handle.net/1805/39406
dc.language.isoen_US
dc.publisherElsevier
dc.relation.isversionof10.1016/j.ebiom.2023.104820
dc.relation.journaleBioMedicine
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.sourcePMC
dc.subjectDeep learning
dc.subjectMachine learning
dc.subjectAlzheimer's disease
dc.subjectMetabolomics
dc.subjectLipidomics
dc.titleCircular-SWAT for deep learning based diagnostic classification of Alzheimer's disease: application to metabolome data
dc.typeArticle
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