Random Forest in Clinical Metabolomics for Phenotypic Discrimination and Biomarker Selection

Metabolomic data analysis becomes increasingly challenging when dealing with clinical samples with diverse demographic and genetic backgrounds and various pathological conditions or treatments. Although many classification tools, such as projection to latent structures (PLS), support vector machine...

Descripción completa

Detalles Bibliográficos
Autores principales: Chen, Tianlu, Cao, Yu, Zhang, Yinan, Liu, Jiajian, Bao, Yuqian, Wang, Congrong, Jia, Weiping, Zhao, Aihua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2013
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3594909/
https://www.ncbi.nlm.nih.gov/pubmed/23573122
http://dx.doi.org/10.1155/2013/298183

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3594909/
https://www.ncbi.nlm.nih.gov/pubmed/23573122
http://dx.doi.org/10.1155/2013/298183

Ejemplares similares