Semi-Supervised Projective Non-Negative Matrix Factorization for Cancer Classification

Advances in DNA microarray technologies have made gene expression profiles a significant candidate in identifying different types of cancers. Traditional learning-based cancer identification methods utilize labeled samples to train a classifier, but they are inconvenient for practical application be...

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Detalles Bibliográficos
Autores principales: Zhang, Xiang, Guan, Naiyang, Jia, Zhilong, Qiu, Xiaogang, Luo, Zhigang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4579132/
https://www.ncbi.nlm.nih.gov/pubmed/26394323
http://dx.doi.org/10.1371/journal.pone.0138814

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4579132/
https://www.ncbi.nlm.nih.gov/pubmed/26394323
http://dx.doi.org/10.1371/journal.pone.0138814

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