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# RLassoCox A reweighted Lasso-Cox model for survival prediction and biomarker discovery. # Details Package: RLassoCox Type: Package Title: A reweighted Lasso-Cox by integrating gene interaction information Version: 0.99.4 Date: 2020-11-20 Authors@R: c(person(given = "Wei", family = "Liu", email = "", role = c("cre", "aut"),comment = c(ORCID = "0000-0002-5496-3641"))) Depends: R (>= 4.1), glmnet Imports: Matrix, igraph, survival, stats Description: RLassoCox is a package that implements the RLasso-Cox model proposed by Wei Liu. The RLasso-Cox model integrates gene interaction information into the Lasso-Cox model for accurate survival prediction and survival biomarker discovery. It is based on the hypothesis that topologically important genes in the gene interaction network tend to have stable expression changes. The RLasso-Cox model uses random walk to evaluate the topological weight of genes, and then highlights topologically important genes to improve the generalization ability of the Lasso-Cox model. The RLasso-Cox model has the advantage of identifying small gene sets with high prognostic performance on independent datasets, which may play an important role in identifying robust survival biomarkers for various cancer types. License: Artistic-2.0 biocViews: Survival, Regression, GeneExpression, GenePrediction, Network BugReports: BiocType: Software Suggests: knitr VignetteBuilder: knitr # Index of help topics: RLassoCox Reweighted Lasso-Cox model cvRLassoCox Cross-validation for the RLasso-Cox model dGMMirGraph The KEGG network g.HuRI.EntrezID The HuRI network mRNA_matrix The expression data predict.RLassoCox Make predictions from a RLasso-Cox model predict.cvRLassoCox Make predictions from a cross-validated RLasso-Cox model rw Directed Random Walk survData Survival data # Examples data(dGMMirGraph) data(mRNA_matrix) data(survData) trainSmpl.Idx <- sample(1:dim(mRNA_matrix)[1], floor(2/3*dim(mRNA_matrix)[1])) testSmpl.Idx <- setdiff(1:dim(mRNA_matrix)[1], trainSmpl.Idx) trainSmpl <- mRNA_matrix[trainSmpl.Idx ,] testSmpl <- mRNA_matrix[testSmpl.Idx ,] res <- RLassoCox(x=trainSmpl, y=survData[trainSmpl.Idx ,], globalGraph=dGMMirGraph) lp <- predict(object = res, newx = testSmpl) cv.res <- cvRLassoCox(x=trainSmpl, y=survData[trainSmpl.Idx ,], globalGraph=dGMMirGraph, nfolds = 5) cv.lp <- predict(object = cv.res, newx = testSmpl, s = "lambda.min")