.png){kind=icon}
返回
Recursive partitioning for tumor classification with gene expression microarray data
.png){kind=icon}
OA
AI
DOI：10.1073/pnas.111153698.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Precise classification of tumors is critically important for cancer diagnosis and treatment. It is also a scientifically challenging task. Recently, efforts have been made to use gene expression profiles to improve the precision of classification, with limited success. Using a published data set for purposes of comparison, we introduce a methodology based on classification trees and demonstrate that it is significantly more accurate for discriminating among distinct colon cancer tissues than other statistical approaches used heretofore. In addition, competing classification trees are displayed, which suggest that different genes may coregulate colon cancers.
Keyword:
OLIGONUCLEOTIDE ARRAYS
CLUSTERING ANALYSIS
PATTERNS
CANCER
CELLS
DIFFERENTIATION
IDENTIFICATION
HYBRIDIZATION
GENOME
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
P
IF:
9.1
论文数:
10.8W
被引数:
73.5W
机构
暂无机构信息
引用论文
.png){kind=icon}
Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization通过微阵列杂交全面鉴定酿酒酵母的细胞周期调控基因OAAI
OAAI分享收藏分享收藏
分享收藏
查看更多内容