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Metagenes and molecular pattern discovery using matrix factorization.
Brunet JP, Tamayo P, Golub TR, Mesirov JP. Brunet JP, et al. Proc Natl Acad Sci U S A. 2004 Mar 23;101(12):4164-9. doi: 10.1073/pnas.0308531101. Epub 2004 Mar 11. Proc Natl Acad Sci U S A. 2004. PMID: 15016911 Free PMC article.
We describe here the use of nonnegative matrix factorization (NMF), an algorithm based on decomposition by parts that can reduce the dimension of expression data from thousands of genes to a handful of metagenes. ...We found it less sensitive to a priori sele …
We describe here the use of nonnegative matrix factorization (NMF), an algorithm based on decomposition by parts that can redu …
Nonnegative matrix factorization: an analytical and interpretive tool in computational biology.
Devarajan K. Devarajan K. PLoS Comput Biol. 2008 Jul 25;4(7):e1000029. doi: 10.1371/journal.pcbi.1000029. PLoS Comput Biol. 2008. PMID: 18654623 Free PMC article. Review.
In the context of a pxn gene expression matrix V consisting of observations on p genes from n samples, each column of W defines a metagene, and each column of H represents the metagene expression pattern of the corresponding sample. ...More recently, i …
In the context of a pxn gene expression matrix V consisting of observations on p genes from n samples, each column of W defines a …
Improving gene expression cancer molecular pattern discovery using nonnegative principal component analysis.
Han X. Han X. Genome Inform. 2008;21:200-11. Genome Inform. 2008. PMID: 19425159 Free article.
Robust cancer molecular pattern identification from microarray data not only plays an essential role in modern clinic oncology, but also presents a challenge for statistical learning. ...The meta-samples are low-dimensional projections of original cancer samples in …
Robust cancer molecular pattern identification from microarray data not only plays an essential role in modern clinic oncology …