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Subtyping via Consensus Factor Analysis (SCFA) can efficiently remove noisy signals from consistent molecular patterns in multi-omics data. SCFA first uses an autoencoder to select only important features and then repeatedly performs factor analysis to represent the data with different numbers of factors. Using these representations, it can reliably identify cancer subtypes and accurately predict risk scores of patients.
Package details |
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Author | Duc Tran [aut, cre], Hung Nguyen [aut], Tin Nguyen [fnd] |
Bioconductor views | Classification Clustering Survival |
Maintainer | Duc Tran <duct@nevada.unr.edu> |
License | LGPL |
Version | 1.0.0 |
URL | https://github.com/duct317/SCFA |
Package repository | View on Bioconductor |
Installation |
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