Improving molecular cancer class discovery through sparse non-negative matrix factorization

By Yuan Gao, George M. Church. Bioinform., 2005

338 citations recorded by the source corpus.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/848899771fa4f6c1ce49a19e675b44b0991ce7be

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

1. Michael R. Stratton
2. Haesun Park
3. Cathal Seoighe
4. Hyunsoo Kim
5. Peter J. Campbell
6. Renaud Gaujoux
7. Karthik Devarajan

In Molecular Biology

1. Haesun Park
2. Yujin Zhang
3. Yu-Xiong Wang
4. Cathal Seoighe
5. Renaud Gaujoux
6. Peter J. Campbell
7. S. Nik-Zainal
8. Ludmil B. Alexandrov

Citations in the corpus

• Nonnegative Matrix Factorization: A Comprehensive Review (2013, background): “The ‘2 norm of V is selected as the sparseness penalty term in the regular SED objective function by Gao and Church [91].”
• Nonnegative Matrix Factorization: An Analytical and Interpretive Tool in Computational Biology (2008, methodology): “As noted before, such localized patterns may point to groups of co-regulated or functionally relevant genes [38,43,47,48,50].”
• Nonnegative Matrix Factorization Based on Alternating Nonnegativity Constrained Least Squares and Active Set Method (2008, methodology): “There are several variations of NMF/NUR [8, 29, 6].”
• Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis (2007, background): “Some other approaches (Gao and Church, 2005; Pauca et al., 2004) imposed sparsity *To whom correspondence should be addressed.”
• Deciphering Signatures of Mutational Processes Operative in Human Cancer (2013, positive): “Algorithms implementing this and other constraints have been previously developed (Berry et al., 2007; Gao and Church, 2005; Peharz and Pernkopf, 2012; Zheng et al., 2006) and could be applied to cancer genomics data.”
• Algorithms for Nonnegative Matrix Factorization with the β-Divergence (2010, background): “…by reflectance spectra analysis (Berry et al., 2007), portfolio diversification (Drakakis et al., 2007), DNA gene expression analysis (Brunet et al., 2004; Gao and Church, 2005), clustering of protein interactions (Greene et al., 2008), image denoising and inpainting (Mairal et al., 2010), etc.”
• A flexible R package for nonnegative matrix factorization (2010, background): “This unique feature is particularly interesting in the context of gene expression microarrays, where overlapping metagenes could identify genes that belong to multiple pathways or processes [4,9].”