Cargando…
Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data
BACKGROUND: Like microarray-based investigations, high-throughput proteomics techniques require machine learning algorithms to identify biomarkers that are informative for biological classification problems. Feature selection and classification algorithms need to be robust to noise and outliers in t...
Autores principales: | Zhang, Xuegong, Lu, Xin, Shi, Qian, Xu, Xiu-qin, Leung, Hon-chiu E, Harris, Lyndsay N, Iglehart, James D, Miron, Alexander, Liu, Jun S, Wong, Wing H |
---|---|
Formato: | Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2006
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1456993/ https://www.ncbi.nlm.nih.gov/pubmed/16606446 http://dx.doi.org/10.1186/1471-2105-7-197 |
Ejemplares similares
-
Alignment-free supervised classification of metagenomes by recursive SVM
por: Cui, Hongfei, et al.
Publicado: (2013) -
Supervised Classification by Filter Methods and Recursive Feature Elimination Predicts Risk of Radiotherapy-Related Fatigue in Patients with Prostate Cancer
por: Saligan, Leorey N, et al.
Publicado: (2014) -
Classification of EEG Using Adaptive SVM Classifier with CSP and Online Recursive Independent Component Analysis
por: Antony, Mary Judith, et al.
Publicado: (2022) -
Recursive Partitioning Method on Competing Risk Outcomes
por: Xu, Wei, et al.
Publicado: (2016) -
Multiclass classification of microarray data samples with a reduced number of genes
por: Tapia, Elizabeth, et al.
Publicado: (2011)