Journal ArticleDOI
Microarray data normalization and transformation
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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.read more
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Proteomic analysis of bovine skeletal muscle hypertrophy
Julien Bouley,Bruno Meunier,Christophe Chambon,Stefaan De Smet,Jean-François Hocquette,Brigitte Picard +5 more
TL;DR: A role for exon 16 of fast troponin T in the physiological adaptation of the fast muscle phenotype is suggested, suggesting that myostatin negatively controls mainly fast‐twitch glycolytic fiber number in Semitendinosus muscle.
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Reconstruction of microbial transcriptional regulatory networks.
TL;DR: Large-scale regulatory network reconstructions can be converted to in silico models that allow systematic analysis of network behavior in response to changes in environmental conditions and can be combined with genome-scale metabolic models to build integrated models of cellular function.
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Inferring Gene Regulatory Networks using Differential Evolution with Local Search Heuristics
Nasimul Noman,Hitoshi Iba +1 more
TL;DR: The proposed fitness function has been found to be more suitable for identifying the correct network topology and for estimating the accurate parameter values compared to the existing ones.
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An HMM approach to genome-wide identification of differential histone modification sites from ChIP-seq data
TL;DR: The proposed approach, ChIPDiff, employs a hidden Markov model (HMM) to infer the states of histone modification changes at each genomic location and demonstrated that the H3K27me3 DHMSs identified by the approach are of high sensitivity, specificity and technical reproducibility.
Journal ArticleDOI
Biological and genetic characteristics of tumor-initiating cells in colon cancer.
Keisuke Ieta,Fumiaki Tanaka,Naotsugu Haraguchi,Yoshiaki Kita,Hiroyuki Sakashita,Koshi Mimori,Toshifumi Matsumoto,Hiroshi Inoue,Hiroyuki Kuwano,Masaki Mori +9 more
TL;DR: It was confirmed that CD133+ cells in colon cancer are useful markers for the detection of tumor-initiating cells and differential gene expression correlating with CD133 expression was found.
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
Samprit Chatterjee,B. Price +1 more
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