Author

Joosung Min

Simon Fraser UniversityORCID 0000-0002-5541-5014Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Research Focus:differential expression· 2 Organism:human· 2 Approach:Bayesian inference· 1 Research Focus:benchmarking· 1 Assay:DNA methylation· 1 Research Focus:gene regulatory network inference· 1 Assay:metabolomics· 1 Approach:statistical model· 1

Frequent Co-Authors

Angela Brooks-WilsoniD· 2 shared Lloyd T. ElliottiD· 2 shared Olga VishnyakovaiD· 2 shared Ann Zenobia MooreiD· 1 shared Kenneth J. RockwoodiD· 1 shared Luigi FerrucciiD· 1 shared T ToshikoiD· 1 shared Xiaowei SongiD· 1 shared

Activity by Year

2026-2026 · peak 2/yr

Co-Author Collaboration Network

8 top collaborators · 28 mutual co-author links

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2 shared papersGenomics

Angela Brooks-WilsonORCID

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2 papers

Recent·Most cited

GenomicsThe Journals of Gerontology Series APeer-reviewedSep 17, 2026

DNA methylation variability provides a complementary epigenetic signature of aging heterogeneity: Findings from the Canadian Longitudinal Study on Aging and the Baltimore Longitudinal Study of Aging

Olga Vishnyakova, Joosung Min, Ann Zenobia Moore…·Lloyd T. Elliott

The paper identifies DNA methylation variability and differential methylation regions associated with aging-related health decline and constructs an epigenetic biomarker that outperforms methylation-only biomarkers in predicting mortality.

DOI 10.1093/gerona/glag234

humanDNA methylation

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BioinformaticsGeroSciencePeer-reviewedMay 21, 2026

A New Sparse Bayesian Quantile Neural Network-based Approach and Its Application to Discover Physiological Sweet Spots in the Canadian Longitudinal Study on Aging

Joosung Min, Olga Vishnyakova, Angela Brooks-Wilson, Lloyd T. Elliott

The paper introduces Q-FSNet and Q-DirichNet, sparse Bayesian neural networks for identifying physiological sweet spots in metabolomic data from the Canadian Longitudinal Study on Aging.

DOI 10.1007/s11357-026-02280-1

humanmetabolomics

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