Cargando…
Identifying Cancer Biomarkers From Microarray Data Using Feature Selection and Semisupervised Learning
Microarrays have now gone from obscurity to being almost ubiquitous in biological research. At the same time, the statistical methodology for microarray analysis has progressed from simple visual assessments of results to novel algorithms for analyzing changes in expression profiles. In a micro-RNA...
Formato: | Online Artículo Texto |
---|---|
Lenguaje: | English |
Publicado: |
IEEE
2014
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4848046/ https://www.ncbi.nlm.nih.gov/pubmed/27170887 http://dx.doi.org/10.1109/JTEHM.2014.2375820 |
Ejemplares similares
-
Online Coregularization for Multiview Semisupervised Learning
por: Sun, Boliang, et al.
Publicado: (2013) -
Self-Trained LMT for Semisupervised Learning
por: Fazakis, Nikos, et al.
Publicado: (2016) -
Iterative Nearest Neighborhood Oversampling in Semisupervised Learning from Imbalanced Data
por: Li, Fengqi, et al.
Publicado: (2013) -
Semisupervised Semantic Segmentation with Mutual Correction Learning
por: Xiao, Yifan, et al.
Publicado: (2022) -
Semisupervised Generative Autoencoder for Single-Cell Data
por: Trong, Trung Ngo, et al.
Publicado: (2020)