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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...

詳細記述

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書誌詳細
出版年:BMC Bioinformatics
主要な著者: Wang, Juan, Lu, Cong-Hai, Liu, Jin-Xing, Dai, Ling-Yun, Kong, Xiang-Zhen
フォーマット: Artigo
言語:Inglês
出版事項: BioMed Central 2019
主題:
オンライン・アクセス:https://ncbi.nlm.nih.gov/pmc/articles/PMC6936083/
https://ncbi.nlm.nih.gov/pubmed/31888442
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/s12859-019-3231-5
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