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Predictive analysis of microarray data [PDF]
Open Journal of Genetics, 2014 Microarray gene expression data are analyzed by means of a Bayesian nonparametric model, with emphasis on prediction of future observables, yielding a method for selection of differentially expressed genes and a ...
F., Paulo C. Marques +1 more
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Knowledge-based analysis of microarray gene expression data by using support vector machines [PDF]
Proceedings of the National Academy of Sciences of the United States of America, 2000 We introduce a method of functionally classifying genes by using gene expression data from DNA microarray hybridization experiments. The method is based on the theory of support vector machines (SVMs).
Michael P. Brown +7 more
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Laplace Approximated EM Microarray Analysis: An Empirical Bayes Approach for Comparative Microarray Experiments [PDF]
Statistical Science 2010, Vol. 25, No. 3, 388-407, 2011 A two-groups mixed-effects model for the comparison of (normalized) microarray data from two treatment groups is considered. Most competing parametric methods that have appeared in the literature are obtained as special cases or by minor modification of ...
Bar, Haim +3 more
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Microarray analysis in pulmonary hypertension [PDF]
European Respiratory Journal, 2016 Microarrays are a powerful and effective tool that allows the detection of genome-wide gene expression differences between controls and disease conditions. They have been broadly applied to investigate the pathobiology of diverse forms of pulmonary hypertension, namely group 1, including patients with idiopathic pulmonary arterial hypertension, and ...
Grazyna Kwapiszewska +3 more
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Coupled Two-Way Clustering Analysis of Gene Microarray Data [PDF]
, 2000 We present a novel coupled two-way clustering approach to gene microarray data analysis. The main idea is to identify subsets of the genes and samples, such that when one of these is used to cluster the other, stable and significant partitions emerge ...
Alizadeh +12 more
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Diverse correlation structures in gene expression data and their utility in improving statistical inference [PDF]
Annals of Applied Statistics 2007, Vol. 1, No. 2, 538-559, 2007 It is well known that correlations in microarray data represent a serious nuisance deteriorating the performance of gene selection procedures. This paper is intended to demonstrate that the correlation structure of microarray data provides a rich source ...
Klebanov, Lev, Yakovlev, Andrei
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Nonparametric estimation of genewise variance for microarray data [PDF]
Annals of Statistics 2010, Vol. 38, No. 5, 2723-2750, 2010 Estimation of genewise variance arises from two important applications in microarray data analysis: selecting significantly differentially expressed genes and validation tests for normalization of microarray data. We approach the problem by introducing a
Fan, Jianqing, Feng, Yang, Niu, Yue S.
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AMADA: analysis of microarray data [PDF]
Bioinformatics, 2001 Abstract Summary: AMADA is a Windows program for identifying co-expressed genes from microarray data. It performs data transformation, principal component analysis, a variety of cluster analyses and extensive graphic functions for visualizing expression profiles.
Xie, Z, Xia, X
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Does Logarithm Transformation of Microarray Data Affect Ranking Order of Differentially Expressed Genes? [PDF]
, 2006 A common practice in microarray analysis is to transform the microarray raw data (light intensity) by a logarithmic transformation, and the justification for this transformation is to make the distribution more symmetric and Gaussian-like.
Li, Wentian +2 more
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