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

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

Abstract 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 smal...

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Bibliographic Details
Main Authors: Juan Wang, Cong-Hai Lu, Jin-Xing Liu, Ling-Yun Dai, Xiang-Zhen Kong
Format: Article
Language:English
Published: BMC 2019-12-01
Series:BMC Bioinformatics
Subjects:
Affinity matrix
Gene expression data
Graph regularization
Symmetric constraint
Low-rank representation
Spectral clustering
Online Access:https://doi.org/10.1186/s12859-019-3231-5

Internet

https://doi.org/10.1186/s12859-019-3231-5

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