Computational Analysis of Microarray Data

By P. Vasisht. 2003

1,011 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/620eae95c5cf0fc40faf4d526ab6fdc45a02f18d

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Citations in the corpus

Three-parameter lognormal distribution ubiquitously found in cDNA microarray data and its application to parametric data treatment (2004, unknown): “In such model-based normalization methods, the simplest assumed stability might be the total amount of mRNA in a tissue, which forms the basis of the normalization of the total intensity [7,10] or the global standardization [11,12].”
So Many Variables: Joint Modeling in Community Ecology. (2015, methodology): “LVMs have previously been used in contexts where the number of response variables is in the thousands, such as in the analysis of microarray data [31], made possible by substantially reducing the number of covariance parameters in the model.”
Statistical tests for differential expression in cDNA microarray experiments (2003, methodology): “For these purposes, we assume that the data to be used are of good quality and have been appropriately transformed (normalized) to ensure that experimentally introduced biases have been removed [1,2].”
Systematic survey reveals general applicability of "guilt-by-association" within gene coexpression networks (2005, background): “Background From the very start of the high-throughput microarray expression revolution it was understood [1,2] that guiltby-association was a powerful heuristic to both explain why genes might have correlated expression in a set of experiments and infer what might be the function of a Published: 14 ”
BioArray Software Environment (BASE): a platform for comprehensive management and analysis of microarray data (2002, methodology): “There are many data-transformation and analysis algorithms that we would like to integrate within BASE as plug-in modules (see [31] for a review), and features we anticipate will be desirable in the future and intend to support (for example, MAGE-ML [30] export for data deposition in public…”
Metabolite profiling: from diagnostics to systems biology (2004, methodology): “The use of two statistical tools, HIERARCHICAL CLUSTER ANALYSIS (HCA) and PRINCIPAL COMPONENT ANALYSIS (PCA; both reviewed in Ref”
A framework to identify physiological responses in microarray-based gene expression studies: selection and interpretation of biologically relevant genes. (2008, methodology): “When SOM analysis is applied to whole genome data sets, unrelated data will also produce clusters, without any physiological relevance (35).”
Clinical biomarkers in drug discovery and development (2003, background): “However, attention must be paid to protocol design and analysis if confounders are to be managed and these huge volumes of data are to be distilled into useful bioinformatic”
Expression profiling of microRNAs by deep sequencing (2009, methodology): “A number of algorithms have been developed for the normalization of array-based gene expression profiles [21]; however, many of these algorithms rely”
Judging the quality of gene expression-based clustering methods using gene annotation. (2002, methodology): “Hierarchical clustering (encompassing single-, complete-, and averagelinkage variants), k-means clustering, and self-organized maps (SOM) are the most widely used in analysis of gene-expression data (for reviews, see Gerstein and Jansen 2000; Quackenbush 2001).”