Taeho Jo, Junpyo Kima, Paula Bice, Kevin Huynh, Tingting Wang, Matthias Arnold, Peter J. Meikle, Corey Giles, Rima Kaddurah-Daoukf, Andrew J. Saykina, Kwangsik Nho, eBioMedicine (2023) This study introduces the Circular-Sliding Window Association Test (c-SWAT), a methodology designed to enhance the diagnostic classification of AD using serum-based metabolomics data, with a focus on lipidomics. Leveraging data from 997 participants, c-SWAT integrates feature correlation analysis, feature selection via CNN, and final classification through Random Forest, achieving an accuracy of up to 80.8% and an AUC of 0.808 in distinguishing AD from cognitively normal older adults.
Taeho Jo, Junpyo Kim, Paula Bice, Kevin Huynh, Tingting Wang, Peter J Meikle, Rima Kaddurah-Daouk, Kwangsik Nho, Andrew J. Saykin, AAIC (2022) We used serum-based cross-sectional lipidome data with 781 lipids from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) including 216 cognitively normal (CN), 635 MCI, and 382 dementia (AD). Phenotype influence scores (PIS) was derived by deep learning-based circling Sliding Window Association Test approach (Circling SWAT), an extension of SWAT (Jo et al., 2022) with correlation heatmap and dendrogram analysis for omics data with minimal features.
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