Cargando…
Clustering of the SOM easily reveals distinct gene expression patterns: results of a reanalysis of lymphoma study
BACKGROUND: A method to evaluate and analyze the massive data generated by series of microarray experiments is of utmost importance to reveal the hidden patterns of gene expression. Because of the complexity and the high dimensionality of microarray gene expression profiles, the dimensional reductio...
Autores principales: | Wang, Junbai, Delabie, Jan, Aasheim, Hans Christian, Smeland, Erlend, Myklebost, Ola |
---|---|
Formato: | Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2002
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC138792/ https://www.ncbi.nlm.nih.gov/pubmed/12445336 http://dx.doi.org/10.1186/1471-2105-3-36 |
Ejemplares similares
-
Tumor classification and marker gene prediction by feature selection and fuzzy c-means clustering using microarray data
por: Wang, Junbai, et al.
Publicado: (2003) -
Quality versus accuracy: result of a reanalysis of protein-binding microarrays from the DREAM5 challenge by using BayesPI2 including dinucleotide interdependence
por: Wang, Junbai
Publicado: (2014) -
Integrating whole genome sequencing, methylation, gene expression, topological associated domain information in regulatory mutation prediction: A study of follicular lymphoma
por: Farooq, Amna, et al.
Publicado: (2022) -
SomInaClust: detection of cancer genes based on somatic mutation patterns of inactivation and clustering
por: Van den Eynden, Jimmy, et al.
Publicado: (2015) -
Research on Clustering Algorithm Based on Improved SOM Neural Network
por: Shi, Chengxiang, et al.
Publicado: (2022)