DOI: 10.18129/B9.bioc.BioMM  

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BioMM: Biological-informed Multi-stage Machine learning framework for phenotype prediction using omics data

Bioconductor version: Development (3.18)

The identification of reproducible biological patterns from high-dimensional omics data is a key factor in understanding the biology of complex disease or traits. Incorporating prior biological knowledge into machine learning is an important step in advancing such research. We have proposed a biologically informed multi-stage machine learing framework termed BioMM specifically for phenotype prediction based on omics-scale data where we can evaluate different machine learning models with prior biological meta information.

Author: Junfang Chen and Emanuel Schwarz

Maintainer: Junfang Chen <junfang.chen33 at>

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biocViews Classification, GO, Genetics, Pathways, Regression, Software
Version 1.17.1
In Bioconductor since BioC 3.9 (R-3.6) (4.5 years)
License GPL-3
Depends R (>= 3.6)
Imports stats, utils, grDevices, lattice, BiocParallel, glmnet, rms, precrec, nsprcomp, ranger, e1071, ggplot2, vioplot, CMplot, imager, topGO, xlsx
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