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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.
Package details |
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Author | Junfang Chen and Emanuel Schwarz |
Bioconductor views | Classification GO Genetics Pathways Regression Software |
Maintainer | Junfang Chen <junfang.chen33@gmail.com> |
License | GPL-3 |
Version | 1.6.0 |
Package repository | View on Bioconductor |
Installation |
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