Cargando…
Bi-dimensional principal gene feature selection from big gene expression data
Gene expression sample data, which usually contains massive expression profiles of genes, is commonly used for disease related gene analysis. The selection of relevant genes from huge amount of genes is always a fundamental process in applications of gene expression data. As more and more genes have...
Autores principales: | Hou, Xiaoqian, Hou, Jingyu, Huang, Guangyan |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9728919/ https://www.ncbi.nlm.nih.gov/pubmed/36477666 http://dx.doi.org/10.1371/journal.pone.0278583 |
Ejemplares similares
-
A greedy feature selection algorithm for Big Data of high dimensionality
por: Tsamardinos, Ioannis, et al.
Publicado: (2018) -
Principal components analysis and the reported low intrinsic dimensionality of gene expression microarray data
por: Lenz, Michael, et al.
Publicado: (2016) -
Feature Selection and Feature Stability Measurement Method for High-Dimensional Small Sample Data Based on Big Data Technology
por: Huang, Chengyuan
Publicado: (2021) -
Characteristic Gene Selection via Weighting Principal Components by Singular Values
por: Liu, Jin-Xing, et al.
Publicado: (2012) -
Benchmark of filter methods for feature selection in high-dimensional gene expression survival data
por: Bommert, Andrea, et al.
Publicado: (2021)