xgboost机器学习算法进行特征基因筛选
简介
利用xgboost机器学习算法,根据生存信息及基因表达数据,进行特征基因筛选。调用xgboost R包
数据说明
数据包括3+N列:第1列是样品,第2列是生存时间,第3列是生存状态(0:生,1:死),第4+列为基因表达。输出为train_error图,vip图及vip表格。通常可以按照vip的拐点作为阈值进行特征筛选。
论文例子
Integrated network analysis to explore the key genes regulated by parathyroid hormone receptor 1 in osteosarcoma Fig 1.
如何引用?
建议直接写网址。助力10000+篇
(google学术),8500+篇
(知网)论文
正式引用:Tang D, Chen M, Huang X, Zhang G, Zeng L, Zhang G, Wu S, Wang Y.
SRplot: A free online platform for data visualization and graphing. PLoS One. 2023 Nov 9;18(11):e0294236. doi: 10.1371/journal.pone.0294236. PMID: 37943830.
方法章节:Heatmap was plotted by https://www.bioinformatics.com.cn (last accessed on May 4, 2026), an online platform for data analysis and visualization.
致谢章节:We thank Mingjie Chen (Shanghai NewCore Biotechnology Co., Ltd.) for providing data analysis and visualization support.