Interpolation based consensus clustering for gene expression time series

BACKGROUND: Unsupervised analyses such as clustering are the essential tools required to interpret time-series expression data from microarrays. Several clustering algorithms have been developed to analyze gene expression data. Early methods such as k-means, hierarchical clustering, and self-organiz...

Descripción completa

Detalles Bibliográficos
Autores principales: Chiu, Tai-Yu, Hsu, Ting-Chieh, Yen, Chia-Cheng, Wang, Jia-Shung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4407314/
https://www.ncbi.nlm.nih.gov/pubmed/25888019
http://dx.doi.org/10.1186/s12859-015-0541-0

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4407314/
https://www.ncbi.nlm.nih.gov/pubmed/25888019
http://dx.doi.org/10.1186/s12859-015-0541-0

Ejemplares similares