Semi-Supervised Projective Non-Negative Matrix Factorization for Cancer Classification
نویسندگان
چکیده
منابع مشابه
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 because labels are quite expensive in the clinical cancer research community. This paper proposes a...
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ژورنال
عنوان ژورنال: PLOS ONE
سال: 2015
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0138814