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Journal ArticleDOI

Microarray data normalization and transformation

John Quackenbush
- 01 Dec 2002 - 
- Vol. 32, Iss: 4, pp 496-501
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TLDR
This review focuses on the much more mundane but indispensable tasks of 'normalizing' data from individual hybridizations to make meaningful comparisons of expression levels, and of 'transforming' them to select genes for further analysis and data mining.
Abstract
Underlying every microarray experiment is an experimental question that one would like to address. Finding a useful and satisfactory answer relies on careful experimental design and the use of a variety of data-mining tools to explore the relationships between genes or reveal patterns of expression. While other sections of this issue deal with these lofty issues, this review focuses on the much more mundane but indispensable tasks of 'normalizing' data from individual hybridizations to make meaningful comparisons of expression levels, and of 'transforming' them to select genes for further analysis and data mining.

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Citations
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Effects of proteasome inhibitor MG-132 on the parasite Schistosoma mansoni.

TL;DR: The genome-wide changes in gene expression of S. mansoni adult worms exposed in vitro to MG-132 suggest that the proteasome might be an important molecular target for the design of new drugs against schistosomiasis.
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Multistrain Genetic Comparisons Reveal CCR5 as a Receptor Involved in Airway Hyperresponsiveness

TL;DR: Using DNA microarrays and knock-out mouse studies, it is shown that CCR5 plays a definitive role in the development of ovalbumin-induced allergic airway inflammatory disease.
Journal ArticleDOI

Deblender: a semi-/unsupervised multi-operational computational method for complete deconvolution of expression data from heterogeneous samples.

TL;DR: Deblender is presented, a flexible complete deconvolution tool operating in semi−/unsupervised mode based on the user’s access to known marker gene lists and information about cell/tissue composition that corroborate that Deblender can be a valuable tool to improve understanding of gene expression datasets with implications for prediction and clinical utilization.
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Orchestrating the Development Lifecycle of Machine Learning-Based IoT Applications: A Taxonomy and Survey

TL;DR: This article provides a comprehensive and systematic survey of the development lifecycle of ML-based IoT applications and outlines the core roadmap and taxonomy and assess and compare existing standard techniques used at individual stages.
Journal ArticleDOI

Microarray analysis reveals that high mobility group A1 is involved in colorectal cancer metastasis.

TL;DR: The present study strongly supports the clinical significance of HMGA1 expression as a predictive indicator of lymph node metastasis in CRC cases, even in submucosal invasive cases which could be cured by local resection.
References
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Journal ArticleDOI

Cluster analysis and display of genome-wide expression patterns

TL;DR: A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression, finding in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function.
Book

Data Reduction and Error Analysis for the Physical Sciences

TL;DR: In this paper, Monte Carlo techniques are used to fit dependent and independent variables least squares fit to a polynomial least-squares fit to an arbitrary function fitting composite peaks direct application of the maximum likelihood.
Journal ArticleDOI

Data Reduction and Error Analysis for the Physical Sciences.

TL;DR: Numerical methods matrices graphs and tables histograms and graphs computer routines in Pascal and Monte Carlo techniques dependent and independent variables least-squares fit to a polynomial least-square fit to an arbitrary function fitting composite peaks direct application of the maximum likelihood.
Journal ArticleDOI

Robust Locally Weighted Regression and Smoothing Scatterplots

TL;DR: Robust locally weighted regression as discussed by the authors is a method for smoothing a scatterplot, in which the fitted value at z k is the value of a polynomial fit to the data using weighted least squares, where the weight for (x i, y i ) is large if x i is close to x k and small if it is not.
Book

Regression Analysis by Example

TL;DR: Simple linear regression Multiple linear regression Regression Diagnostics: Detection of Model Violations Qualitative Variables as Predictors Transformation of Variables Weighted Least Squares The Problem of Correlated Errors Analysis of Collinear Data Biased Estimation of Regression Coefficients Variable Selection Procedures Logistic Regression Appendix References as discussed by the authors
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