Metagenes and molecular pattern discovery using matrix factorization

By Jean-Philippe Brunet, Pablo Tamayo, Todd R. Golub, Jill P. Mesirov. Proceedings of the National Academy of Sciences of the United States of America, 2004

2,075 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/8467c65182dcbd3ea76aa31c2074930b5e3c1e05

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Citations in the corpus

Co-occurring genomic alterations define major subsets of KRAS - mutant lung adenocarcinoma with distinct biology, immune profiles, and therapeutic vulnerabilities (2015, positive): “In order to identify naturally occurring biologic patterns, we employed non-negative matrix factorization (NMF)—an unsupervised approach—followed by consensus clustering, an iterative process that assesses the stability of partitioning over a number of clustering runs (13, 14).”
K-SVD and its non-negative variant for dictionary design (2005, background): “This subject is often referred to in literature as ‘Non-Negative Matrix Factorization’, or NMF, computing both the dictionary and the coefcient matrices, whose product approximates the signal matrix Y DX. Application for NMF are many and include dimensionality reduction [ 17 ] and analysis of data s”
Densely interconnected transcriptional circuits control cell states in human hematopoiesis (2011, positive): “These ﬁndings are also apparent in a systematic unsupervised analysis using nonnegative matrix factorization (NMF) (Brunet et al., 2004) (Figure S1A) and hierarchical clustering (Figure S1B).”
Multi-View Clustering via Joint Nonnegative Matrix Factorization (2013, background): “Experimental results on synthetic and several real datasets demonstrate the effectiveness of our approach.”
Subtypes of Pancreatic Ductal Adenocarcinoma and Their Differing Responses to Therapy (2011, methodology): “We then performed non-negative matrix factorization (NMF) analysis with consensus clusterin”
Integrative molecular and clinical modeling of clinical outcomes to PD-1 blockade in metastatic melanoma patients (2019, positive): “Mutational Signatures— De novo mutational signatures were generated in this cohort using an adaptation of NMF 70 using the Brunet update method 71 as previously described in detail 51 , using the R-packages SomaticSignatures 72 and NMF 73 .”
Discovery of multi-dimensional modules by integrative analysis of cancer genomic data (2012, methodology): “The time complexityof the joint NMF decomposition isO(tK(M +N1+N2+N3)2) which is similar to that of the original NMF model, wheret is the number of iterations.”
Metaface learning for sparse representation based face recognition (2010, methodology): “Output that identity(y)=argmin ri(y).”
SomaticSignatures: inferring mutational signatures from single-nucleotide variants (2014, background): “Non-negative matrix factorization (NMF) (Brunet et al., 2004) is motivated by the fact that the mutational spectrum fulfills Mij ≥ 0, and imposes the same requirement on the elements of W and H.”