Testing for Differentially-Expressed Genes by Maximum-Likelihood Analysis of Microarray Data

By Trey Ideker, Vesteinn Thorsson, Andrew F. Siegel, Leroy Hood. J. Comput. Biol., 2000

331 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/59f769a1deea3bd8121dbd23e0f16d3ec56b945f

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In Molecular Biology

Citations in the corpus

Detecting differential gene expression with a semiparametric hierarchical mixture method. (2004, background): “Further, many proposed models of expression variability encode approximately constant CV (Chen et al., 1997; Ideker et al., 2000; Baggerly et al., 2001; Li and Wong, 2001; Rocke and Durbin, 2001; Theilhaber et al., 2001; Tsodikov et al., 2002).”
Complementary Profiling of Gene Expression at the Transcriptome and Proteome Levels in Saccharomyces cerevisiae*S (2002, methodology): “From one set of the cultures total RNA was harvested, the poly(A) RNA was purified, and cDNA was synthesized by reverse-transcription as described previously (26).”
Discovering regulatory and signalling circuits in molecular interaction networks (2002, unknown): “‡ Available at http://www.systemsbiology.org/VERAandSAM.”
Gene co-expression network topology provides a framework for molecular characterization of cellular state (2004, background): “Significance analysis has provided insight into specific cellular states by identifying individual genes with corresponding altered expression (Ideker et al., 2000; Rocke and Durbin, 2001).”
The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo (2006, methodology): “The significance of change was estimated by a maximum likelihood method [ 32 ], and subsequent filtering of genes with significant change was performed such that genes with fewer than five conditions with λ values greater than 15 were removed and set aside as genes for which we observed no significa”
Bayesian infinite mixture model based clustering of gene expression profiles (2002, unknown): “approaches have been proposed (Kerr et al., 2000; Newton et al., 2001; Ideker et al., 2000; Wolfinger et al., 2001; Rocke and Dubin, 2001; Lonnstedt and Speed, 2001).”
A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments (2002, methodology): “Chen et al., 1997; Efron et al., 2000; Ideker et al., 2000; Newton et al., 2001; Tusher et al., 2001; Lin et al., 2001; Pan et al., 2001a).”
Microarray data normalization and transformation (2002, background): “For example, there are specific transformations that have been developed for analysis of data from particular platform”
From patterns to pathways: gene expression data analysis comes of age (2002, methodology): “Other model-based methods designed for two-color arrays 10,11 also incorporate data-derived estimates of variation.”
A new approach to decoding life: systems biology. (2001, background): “Finally, deciding whether an expression level changes significantly can be a difficult problem and is also an active area of research (27, 57, 65, 69, 84).”
Prediction by Supervised Principal Components (2006, methodology): “Identication of the most \signican t" genes is an important problem in microarray analysis; several methods have been proposed for this ( Ideker et al. 2000, Kerr et al. 2000, Newton et al. 2001, Tusher et al. 2001b).”
Robust computational reconstitution – a new method for the comparative analysis of gene expression in tissues and isolated cell fractions (2006, methodology): “The λ-values of V&S are based on an expression level-dependent error model [ 25 ].”