Cargando…
Identifying cancer biomarkers by network-constrained support vector machines
BACKGROUND: One of the major goals in gene and protein expression profiling of cancer is to identify biomarkers and build classification models for prediction of disease prognosis or treatment response. Many traditional statistical methods, based on microarray gene expression data alone and individu...
Autores principales: | Chen, Li, Xuan, Jianhua, Riggins, Rebecca B, Clarke, Robert, Wang, Yue |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2011
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3214162/ https://www.ncbi.nlm.nih.gov/pubmed/21992556 http://dx.doi.org/10.1186/1752-0509-5-161 |
Ejemplares similares
-
CyNetSVM: A Cytoscape App for Cancer Biomarker Identification Using Network Constrained Support Vector Machines
por: Shi, Xu, et al.
Publicado: (2017) -
Classification and biomarker identification using gene network modules and support vector machines
por: Yousef, Malik, et al.
Publicado: (2009) -
Motif-guided sparse decomposition of gene expression data for regulatory module identification
por: Gong, Ting, et al.
Publicado: (2011) -
Weighted K-means support vector machine for cancer prediction
por: Kim, SungHwan
Publicado: (2016) -
Prediction of piRNAs using transposon interaction and a support vector machine
por: Wang, Kai, et al.
Publicado: (2014)