ArticleMethods in enzymology2009
Matrix factorization for recovery of biological processes from microarray data.
Article in Methods in enzymology, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- PatternMarkers & GWCoGAPS for novel data-driven biomarkers via whole transcriptome NMF.Bioinformatics (Oxford, England) · 2017Article
- Computational dynamic approaches for temporal omics data with applications to systems medicine.BioData mining · 2017Review
- Toward Signaling-Driven Biomarkers Immune to Normal Tissue Contamination.Cancer informatics · 2016Article
- Satb1 regulates the self-renewal of hematopoietic stem cells by promoting quiescence and repressing differentiation commitment.Nature immunology · 2013Article
- Preferential activation of the hedgehog pathway by epigenetic modulations in HPV negative HNSCC identified with meta-pathway analysis.PloS one · 2013Article
- Article
- Matrix Factorization for Transcriptional Regulatory Network Inference.IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology proceedings. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology · 2012Article
- Systems Approach to Identifying Relevant Pathways from Phenotype Information in Dose-Dependent Time Series Microarray Data.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2011Article
- CoGAPS: an R/C++ package to identify patterns and biological process activity in transcriptomic data.Bioinformatics (Oxford, England) · 2010Article
- Knowledge-based data analysis comes of age.Briefings in bioinformatics · 2010Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
We explore a number of matrix factorization methods in terms of their ability to identify signatures of biological processes in a large gene expression study. We focus on the ability of these methods to find signatures in terms of gene ontology enhancement and on the interpretation of these signatures in the samples. Two Bayesian approaches, Bayesian Decomposition (BD) and Bayesian Factor Regression Modeling (BFRM), perform best. Differences in the strength of the signatures between the samples suggest that BD will be most useful for systems modeling and BFRM for biomarker discovery.
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Registered trials
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