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DESCRIPTION
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DESCRIPTION
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Package: wMKL
Version: 1.0.0
Date: 2022-11-1
Title:
wMKL: a weighted multi-kernel learning model
Authors@R: c(person("Hongyan", "Cao", role=c("aut"), email="caohy@sxmu.edu.cn"),
person("Yuehua", "Cui", role=c("aut", "cre"), email="cuiy@msu.edu"))
Maintainer: Hongyan Cao <caohy@sxmu.edu.cn>
Depends:
R (>= 3.6),
Imports:
parallel,
Matrix,
stats,
methods,
Rcpp,
pracma,
RcppAnnoy,
RSpectra
Suggests:
BiocGenerics,
testthat (>= 3.0.0),
knitr,
igraph
Description:
Cancer is known as the heterogenous disease driven by complex molecular alterations.
Cancer subtypes determined from multi-omics data integration analysis can provide novel insight for personalized medicine.
It is recognized that incorporating prior weight into multi-omics data integration can improve disease subtyping.
Here we develop a weighted method, termed as prior-weight-boosted Multi-Kernel Learning (wMKL)
which can incorporate heterogenous data types as well as flexible weight functions,
to boost subtype identification. Given a series of weight functions,
we propose an omnibus combination strategy to integrate different weight related p-values.
The integrated p-values for features are then applied to update the weighted similarity kernel.
Encoding: UTF-8
LazyData: TRUE
License: GPL
biocViews: Clustering, CancerData
RoxygenNote: 7.2.1
LinkingTo: Rcpp
NeedsCompilation: yes
VignetteBuilder: knitr
Config/testthat/edition: 3