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

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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Bibliographic Details
Main Authors: Xiang Zhang, Naiyang Guan, Zhilong Jia, Xiaogang Qiu, Zhigang Luo
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2015-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC4579132?pdf=render

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http://europepmc.org/articles/PMC4579132?pdf=render

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