Package: BioM2
Title: Biologically Explainable Machine Learning Framework
Version: 1.0.1
Author: Shunjie Zhang and Junfang Chen
Maintainer: Shunjie Zhang <zhang.shunjie@qq.com>
Description: Biologically Explainable Machine Learning Framework for Phenotype Prediction using omics data described in Chen and Schwarz (2017) <arXiv:1712.0036v1>.Identifying reproducible and interpretable biological patterns from high-dimensional omics data is a critical factor in understanding the risk mechanism of complex disease. As such, explainable machine learning can offer biological insight in addition to personalized risk scoring.In this process, a feature space of biological pathways will be generated, and the feature space can also be subsequently analyzed using WGCNA-based (Described in Horvath and Zhang (2005) <doi:10.2202/1544-6115.1128> and Langfelder and Horvath (2008) <doi:10.1186/1471-2105-9-559> ) methods.
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.2.3
Imports: WGCNA, mlr3, CMplot, RColorBrewer, ROCR, caret, dplyr,
        ggplot2, ggpubr, ggthemes, htmlwidgets, jiebaR, mlr3verse,
        parallel, tm, uwot, webshot, wordcloud2
Depends: R (>= 4.1.0)
LazyData: true
NeedsCompilation: no
Packaged: 2023-09-24 04:52:23 UTC; Shedom
Repository: CRAN
Date/Publication: 2023-09-27 07:30:02 UTC
Built: R 4.2.0; ; 2023-09-29 13:09:04 UTC; unix
