Cargando…
A Hybrid Approach for Biomarker Discovery from Microarray Gene Expression Data for Cancer Classification
Microarrays allow researchers to monitor the gene expression patterns for tens of thousands of genes across a wide range of cellular responses, phenotype and conditions. Selecting a small subset of discriminate genes from thousands of genes is important for accurate classification of diseases and ph...
Autores principales: | Peng, Yanxiong, Li, Wenyuan, Liu, Ying |
---|---|
Formato: | Texto |
Lenguaje: | English |
Publicado: |
Libertas Academica
2007
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2675487/ https://www.ncbi.nlm.nih.gov/pubmed/19458773 |
Ejemplares similares
-
Biomarker Discovery Based on Hybrid Optimization Algorithm and Artificial Neural Networks on Microarray Data for Cancer Classification
por: Moteghaed, Niloofar Yousefi, et al.
Publicado: (2015) -
A Hybrid Machine Learning Approach to Screen Optimal Predictors for the Classification of Primary Breast Tumors from Gene Expression Microarray Data
por: Alromema, Nashwan, et al.
Publicado: (2023) -
Collaborative representation-based classification of microarray gene expression data
por: Shen, Lizhen, et al.
Publicado: (2017) -
Deep learning techniques for cancer classification using microarray gene expression data
por: Gupta, Surbhi, et al.
Publicado: (2022) -
Discovery of prognostic biomarkers for predicting lung cancer metastasis using microarray and survival data
por: Huang, Hui-Ling, et al.
Publicado: (2015)