Fundamental patterns underlying gene expression profiles: simplicity from complexity.

By Neal S. Holter, Madhusmita Mitra, Amos Maritan, Marek Cieplak, Jayanth R. Banavar, Nina V. Fedoroff. Proceedings of the National Academy of Sciences of the United States of America, 2000

528 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/a8b5cba1bbb6d0337575aae889121a0a6aebb728

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Citations in the corpus

Analyzing and modeling real-world phenomena with complex networks: a survey of applications (2007, methodology): “Databases with time evolution of gene expression activity are being developed [402, 403].”
Principal component analysis for clustering gene expression data (2001, background): “There is evidence that the sine function correctly models the cell cycle behavior (see Holter et al., 2000 and Alter et al., 2000).”
Spectral biclustering of microarray data: coclustering genes and conditions. (2003, methodology): “The SVD has been applied to microarray experiment analysis in order to find underlying temporal and tumor patterns (Alter et al. 2000; Holter et al. 2000; Raychaudhuri et al. 2000; Lian et al. 2001).”
ChIP-Seq of transcription factors predicts absolute and differential gene expression in embryonic stem cells (2009, methodology): “PCA has been used to capture characteristic modes of gene expression profiles, where the principal components are called eigengenes (36, 37), and to study the clustering property of TFs based on their genomic distributions (38).”
Iterative signature algorithm for the analysis of large-scale gene expression data. (2002, unknown): “However, while standard tools, like clustering algorithms [7, 8, 9, 10, 11, 12, 13, 14] (see [15, 16] for reviews) and Singular Value Decomposition (SVD) [17,18], provide interesting results when applied to relatively small data sets, typically containing tens of experimental conditions and at most ”
Analyzing time series gene expression data (2004, methodology): “Alter et al. (2000) and Holter et al. (2000) used SVD to determine the different phases in the yeast cell cycle, and to order genes in the two-dimensional plane based on these phases.”
Aligning gene expression time series with time warping algorithms (2001, methodology): “, 1998) and SVD (Alter et al., 2000; Holter et al., 2000; Raychaudhuri et al., 2000), it is not a method of decomposing signals into a weighted sum of basis functions or vectors and simplifying them by focusing on principal terms.”
CUR matrix decompositions for improved data analysis (2009, methodology): “The truncated SVD has also been applied to gene expression data (3, 5, 28, 29) in systems biology applications (where one wants to understand the relationship between actual genes) and in clinical applications (where one wants to classify tissue samples from actual patients with and without a…”
Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis (2007, background): “Major sources of expression variation are due to technical [3,4], environmental [5,6], demographic [7,8], or genetic [9–”
Singular value decomposition and principal component analysis (2003, methodology): “SVD and related methods are particularly valuable analysis methods when the distribution of genes is more complicated than the simple distributions in our example data: for instance, SVD has been used to characterize ring-like distributions of genes such as are observed in scatter plots of cell-cycl”