Cell and tumor classification using gene expression data: Construction of forests
AUTOR(ES)
Zhang, Heping
FONTE
The National Academy of Sciences
RESUMO
The advent of gene chips has led to a promising technology for cell, tumor, and cancer classification. We exploit and expand the methodology of recursive partitioning trees for tumor and cell classification from microarray gene expression data. To improve classification and prediction accuracy, we introduce a deterministic procedure to form forests of classification trees and compare their performance with extant alternatives. When two published and commonly used data sets are used, we find that the deterministic forests perform similarly to the random forests in terms of the error rate obtained from the leave-one-out procedure, and all of the forests are far better than the single trees. In addition, we provide graphical presentations to facilitate interpretation of complex forests and compare our findings with the current biological literature. In addition to numerical improvement, the main advantage of deterministic forests is reproducibility and scientific interpretability of all steps in tree construction.
ACESSO AO ARTIGO
http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=153066Documentos Relacionados
- Screening large-scale association study data: exploiting interactions using random forests
- Recursive partitioning for tumor classification with gene expression microarray data
- Tumor classification and marker gene prediction by feature selection and fuzzy c-means clustering using microarray data
- Exploring Expression Data: Identification and Analysis of Coexpressed Genes
- Multi-class cancer classification by total principal component regression (TPCR) using microarray gene expression data