Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Cong-Hai Lu"'
Publikováno v:
BMC Bioinformatics, Vol 22, Iss S12, Pp 1-22 (2022)
Abstract Background The identification of cancer types is of great significance for early diagnosis and clinical treatment of cancer. Clustering cancer samples is an important means to identify cancer types, which has been paid much attention in the
Externí odkaz:
https://doaj.org/article/2c2e2343b2a04305abfed7fff186e7db
Publikováno v:
BMC Bioinformatics, Vol 20, Iss S22, Pp 1-15 (2019)
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
Externí odkaz:
https://doaj.org/article/f7ec37fe558a4e04915befae04efe056
Publikováno v:
Complexity, Vol 2020 (2020)
Low-Rank Representation (LRR) is a powerful subspace clustering method because of its successful learning of low-dimensional subspace of data. With the breakthrough of “OMics” technology, many LRR-based methods have been proposed and used to canc
Externí odkaz:
https://doaj.org/article/89569e2dca004293a6812acf781992a8
Publikováno v:
Complexity, Vol 2020 (2020)
Low-Rank Representation (LRR) is a powerful subspace clustering method because of its successful learning of low-dimensional subspace of data. With the breakthrough of “OMics” technology, many LRR-based methods have been proposed and used to canc
Publikováno v:
BMC Bioinformatics
BMC Bioinformatics, Vol 22, Iss S12, Pp 1-22 (2022)
BMC Bioinformatics, Vol 22, Iss S12, Pp 1-22 (2022)
Background The identification of cancer types is of great significance for early diagnosis and clinical treatment of cancer. Clustering cancer samples is an important means to identify cancer types, which has been paid much attention in the field of
Publikováno v:
BMC Bioinformatics, Vol 20, Iss S22, Pp 1-15 (2019)
BMC Bioinformatics
BMC Bioinformatics
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 cha
Publikováno v:
Frontiers in Genetics
Frontiers in Genetics, Vol 10 (2020)
Frontiers in Genetics, Vol 10 (2020)
As an important approach to cancer classification, cancer sample clustering is of particular importance for cancer research. For high dimensional gene expression data, examining approaches to selecting characteristic genes with high identification fo