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济南烨铭网站建设,网站建设网络推广公司,云南建设厅网站安全员报名入口,seo搜索引擎优化是什么介绍 R 语言包 BioPred 提供了一系列用于精准医疗中的亚组分析和生物标志物分析的工具。它借助极端梯度提升（XGBoost）算法，并结合倾向得分加权和 A 学习方法，帮助优化个体化治疗规则，从而简化亚组识别过程。BioPred 还…

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介绍

R 语言包 BioPred 提供了一系列用于精准医疗中的亚组分析和生物标志物分析的工具。它借助极端梯度提升（XGBoost）算法，并结合倾向得分加权和 A 学习方法，帮助优化个体化治疗规则，从而简化亚组识别过程。BioPred 还能够识别预测性生物标志物，并获取其重要性排名。此外，该包还提供了针对生物标志物分析定制的图形图表。这一工具使临床研究人员能够加深对药物开发中生物标志物和患者群体的理解。

The R package BioPred offers a suite of tools for subgroup and biomarker analysis in precision medicine. Leveraging Extreme Gradient Boosting (XGBoost) along with propensity score weighting and A-learning methods, BioPred facilitates the optimization of individualized treatment rules to streamline subgroup identification. BioPred also enables the identification of predictive biomarkers and obtaining their importance rankings. Moreover, the package provides graphical plots tailored for biomarker analysis. This tool enables clinical researchers seeking to enhance their understanding of biomarkers and patient population in drug development.

代码

https://github.com/deeplearner0731/BioPred

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文章目录

- 介绍
- 代码
- 案例
- 参考

案例

安装

install.packages("BioPred")devtools::install_github("deeplearner0731/BioPred")

运行代码: https://cran.r-project.org/web/packages/BioPred/vignettes/Tutorial.html

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model = XGBoostSub_bin(X, y, trt, pi,Loss_type = “A_learning”, params = list(learning_rate = 0.01, max_depth = 1, lambda = 5, tree_method = ‘hist’), nrounds = 300, disable_default_eval_metric = 0, verbose = FALSE)get_subgroup_results(model, X)eval_metric_bin(model, X, y, pi, trt, Loss_type = “A_learning”)predictive_biomarker_imp(model)fixcut_bin(yvar=“y”, xvar=“x1”, dir=“>“, cutoffs=c(0.1,0.3,0.5), data=tutorial_data, method=“Fisher”, yvar.display=“y”, xvar.display=“Biomarker x1”, vert.x=F)res=cut_perf(yvar=“y”, censorvar=NULL, xvar=“x1”, cutoff=c(0.5), dir=“>”, xvars.adj=NULL, data=tutorial_data, type=“c”, yvar.display=“y”, xvar.display=“Biomarker x1”)res = subgrp_perf_pred(yvar=“y.time”, censorvar=“y.event”, grpvar=“biogroup”, grpname=c(“biomarker_positive”,‘biomarker_negative’),trtvar=“treatment_categorical”, trtname=c(“Placebo”, “Treatment”), xvars.adj=NULL,data=tutorial_data, type=“s”)gam_ctr_plot(yvar=“y.time”, censorvar=“y.event”, xvar= “x1”, xvars.adj=NULL,sxvars.adj=NULL,trtvar=“trt”,type=“s”,data=tutorial_data, k=5, title=“Group Contrast”, ybreaks=NULL, xbreaks=NULL, rugcol.var=NULL,link.scale=T, prt.sum=T, prt.chk=F, outlier.rm=F)roc_bin_plot(yvar=“y”, xvars=“x1”, dirs=“auto”, data=tutorial_data, yvar.display=“y.bin”, xvars.display=“Biomarker x1”)