Gene expression data analysis.

By Alvis Brazma, Jaak Vilo. Microbes and infection, 2001

379 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/8fb2972c26cecdf6c6ffd259ade8bcf46f157cce

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In Molecular Biology

Citations in the corpus

Metabolomics – the link between genotypes and phenotypes (2004, background): “The next question is, do any relationships between the variables exist? Again, practitioners of metabolome analysis might learn from approaches of mRNA expression experiments, which have been summarized in recent reviews (Bittner et al., 1999; Brazma and Vilo, 2000).”
CLICK and EXPANDER: a system for clustering and visualizing gene expression data (2003, background): “Several studies have shown that co-expressed genes tend to share common regulatory elements in their promoter regions (Tavazoie et al., 1999; Zhang, 1999; Brazma and Vilo, 2000).”
J-Express: exploring gene expression data using Java (2001, background): “This data needs to be analyzed and visualized in order for it to help uncover new biological knowledge (see e.g. Brazma and Vilo, 2000 ).”
Analysis of microarray data using Z score transformation. (2003, background): “It is hoped that mining of microarray datasets will lead to the discovery of pathways of common and unique gene expression among different cells, tissues, and disease states.(2) The use of cDNA arrays among individual investigators, between laboratories, and across disciplines has necessitated the r”
A new approach to decoding life: systems biology. (2001, methodology): “Methods for analyzing expression data, including gene-expression clustering and its extensions, have been extensively reviewed elsewhere (21, 30, 64, 108, 141).”
Navigating gene expression using microarrays — a technology review (2001, background): “During the past two years several new strategies for cluster analysis of microarray data have been described (reviewed in ref”
Ensemble machine learning on gene expression data for cancer classification. (2003, methodology): “Thus, machine learning and other artificial intelligence techniques have been widely used to classify or characterise gene expression data (Golub et al 1999; Ben-dor et al 2000; Brazma and Vilo 2000; Brown et al 2000; Li and Wong 2002; Shipp et al 2002).”
LSimpute: accurate estimation of missing values in microarray data with least squares methods. (2004, methodology): “The data produced by microarray experiments can be analyzed by various methods to order and visualize the information inherent in the data (7).”
A higher-order background model improves the detection of promoter regulatory elements by Gibbs sampling (2001, background): “…* To whom correspondence should be addressed. for the computational biologist: detecting novel regulatory elements (motifs) in such sets of coexpressed genes (Brazma and Vilo, 2000; Bucher, 1999; Chu et al., 1998; DeRisi et al., 1997; Spellman et al., 1998; Wolfsberg et al., 1999; Zhang, 1999).”
Filter versus wrapper gene selection approaches in DNA microarray domains (2004, methodology): “The analysis frequently involves class prediction (supervised classiﬁcation), regression, feature selection (in this case, gene selection), outlier detection, principal component analysis, discovering of gene relationships and cluster analysis (unsupervised classiﬁcation) [7].”
A balanced iterative random forest for gene selection from microarray data (2013, methodology): “This dataset is used in many different research papers on feature selection of gene expression datasets [15-17].”
A Paradigm for Class Prediction Using Gene Expression Profiles (2002, neutral): “Predictors built using expression data must be restricted in complexity, with the competing demands of simplicity and accuracy properly balanced (Brazma and Vilo, 2000).”
Discovering the false discovery rate (2010, background): “marginal 1−q|S|=m confidence intervals are constructed for the |S| selected parameters, where S is the (data-dependent) selected set. Working on FCR revealed to us that simultaneous and selective inference, the traditional justifications for multiple-comparisons procedures, are two distinct goals. T”