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Models for microarray gene expression data
Journal of Biopharmaceutical Statistics, 2002This paper describes a general methodology for the analysis of differential gene expression based on microarray data. First, we characterize the data by a linear statistical model that accounts for relevant sources of variation in the data and then we consider estimation of the model parameters. Because microarray studies typically involve thousands of
Mei-Ling Ting, Lee +3 more
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Classification with Gene Expression Data
2005A survey is given of tasks related to the construction and evaluation of classifiers applied to a renal cell cancer data set. Balanced sample splitting, non-specific filtering, linear discriminant analysis, nearest-neighbor prediction, and support vector machines are all concretely illustrated using the MLInterfaces package. Evaluations based on single
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Cancer classification using gene expression data
Information Systems, 2003zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Lu, Ying, Han, Jiawei
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2019
The objectives of this chapter are to teach generating DEGs in microarray gene expression data, extracting a gene cluster of genes with similar patterns of expression, classifying the observed data using SVM and KNN, and learning the basic syntax of the R program, a useful tool for genome data analysis.
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The objectives of this chapter are to teach generating DEGs in microarray gene expression data, extracting a gene cluster of genes with similar patterns of expression, classifying the observed data using SVM and KNN, and learning the basic syntax of the R program, a useful tool for genome data analysis.
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Dealing with gene expression missing data
IEE Proceedings - Systems Biology, 2006Compared evaluation of different methods is presented for estimating missing values in microarray data: weighted K-nearest neighbours imputation (KNNimpute), regression-based methods such as local least squares imputation (LLSimpute) and partial least squares imputation (PLSimpute) and Bayesian principal component analysis (BPCA).
L P, Brás, J C, Menezes
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Determination of tumour marker genes from gene expression data
Drug Discovery Today, 2005Cancer classification has traditionally been based on the morphological study of tumours. However, tumours with similar histological appearances can exhibit different responses to therapy, indicating differences in tumour characteristics on the molecular level.
Cuperlovic-Culf, Miroslava +2 more
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CONTEXT-SPECIFIC GENE REGULATIONS IN CANCER GENE EXPRESSION DATA
Biocomputing 2009, 2008Learning or inferring networks of genomic regulation specific to a cellular state, such as a subtype of tumor, can yield insight above and beyond that resulting from network-learning techniques which do not acknowledge the adaptive nature of the cellular system.
Ina, Sen +5 more
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Calibration of Microarray Gene-Expression Data
2009Calibration of microarray measurements aims at removing systematic biases from the probe-level data to get expression estimates that linearly correlate with the transcript abundance in the studied samples. The improvement of calibration methods is an essential prerequisite for estimating absolute expression levels, which, in turn, are required for ...
Hans, Binder +2 more
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Assessing reliability of gene clusters from gene expression data
Functional & Integrative Genomics, 2000The rapid development of microarray technologies has raised many challenging problems in experiment design and data analysis. Although many numerical algorithms have been successfully applied to analyze gene expression data, the effects of variations and uncertainties in measured gene expression levels across samples and experiments have been largely ...
K, Zhang, H, Zhao
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Normalization of Gene-Expression Microarray Data
2010Expression microarrays are designed to quantify the amount of mRNA in a specific sample. However, this can only be done indirectly through quantifying the color intensities returned by labeled mRNA molecules bound to the array surface. Translating pixel intensities into transcript expression requires a series of computations, generically known as ...
CALZA, Stefano, Yudi Pawitan
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