Multi-cancer samples clustering via graph regularized low-rank representation method under sparse and symmetric constraints

BACKGROUND: Identifying different types of cancer based on gene expression data has become hotspot in bioinformatics research. Clustering cancer gene expression data from multiple cancers to their own class is a significance solution. However, the characteristics of high-dimensional and small sample...

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Detalles Bibliográficos
Autores principales: Wang, Juan, Lu, Cong-Hai, Liu, Jin-Xing, Dai, Ling-Yun, Kong, Xiang-Zhen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6936083/
https://www.ncbi.nlm.nih.gov/pubmed/31888442
http://dx.doi.org/10.1186/s12859-019-3231-5

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6936083/
https://www.ncbi.nlm.nih.gov/pubmed/31888442
http://dx.doi.org/10.1186/s12859-019-3231-5

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