JDSNMF: Joint Deep Semi-Non-Negative Matrix Factorization for Learning Integrative Representation of Molecular Signals in Alzheimer’s Disease
Open accessAbstract
High dimensional multi-omics data integration can enhance our understanding of the complex biological interactions in human diseases. However, most studies involving unsupervised integration of multi-omics data focus on linear integration methods. In this study, we propose a joint deep semi-non-negative matrix factorization (JDSNMF) model, which uses a hierarchical non-linear feature extraction approach that can capture shared latent features from the complex multi-omics data. The extracted latent features obtained from JDSNMF enabled a variety of downstream tasks, including prediction of disease and module analysis. The proposed model is applicable not only to sample-matched multiple data (e.g., multi-omics data from one cohort) but also to feature-matched multiple data (e.g., omics data from multiple cohorts), and therefore it can be flexibly applied to various cases. We demonstrate the capabilities of JDSNMF using sample-matched simulated data and feature-matched multi-omics data from Alzheimer’s disease cohorts, evaluating the feature extraction performance in the context of classification. In a test application, we identify AD- and age-related modules from the latent matrices using an explainable artificial intelligence and regression model. These results show that the JDSNMF model is effective in identifying latent features having a complex interplay of potential biological signatures.
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JDSNMF captures shared latent features across omics layers, enabling the identification of biologically relevant modules for disease prediction.
“Joint Deep Semi-Non-Negative Matrix Factorization (JDSNMF) captures shared latent features across omics layers, enabling the identification of biologically relevant modules for disease prediction243.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [243]
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- Authors
- Moon, S., & Lee, H.
- Affiliations
- Gwangju Institute of Science and Technology (KR)
- Corresponding author
- Lee, Hyunju
- Venue
- Journal of Personalized Medicine
- Publisher
- MDPI AG
- Published
- 2021-07-21
- Volume
- 11
- Issue
- 8
- Pages
- 686
- ISSN
- 2075-4426 (ISSN-L)
- PMC
- PMC8400727
- OpenAlex
- W3183221166
- Cited by
- 19
- References
- 45
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 4 · 2023: 2 · 2022: 4
- FWCI
- 0.78
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Journal of Personalized Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Research Foundation of Korea (NRF-2018M3C7A1054935)
- Korea Health Industry Development Institute (HI18C0460)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- multi-omics integration, Alzheimer's disease, molecular signals, latent feature extraction, disease prediction, explainable AI, deep matrix factorization
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