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Model selection and efficiency testing for normalization of cdna microarray data

by Matthias E. Futschik, Toni Crompton - Genome Biol , 2004
"... We present in this study two novel normalization schemes for cDNA microarrays. They are based on iterative local regression and optimization of model parameters by generalized cross-validation. Permutation tests assessing the efficiency of normalization demonstrated that the proposed schemes have an ..."
Abstract - Cited by 11 (1 self) - Add to MetaCart
We present in this study two novel normalization schemes for cDNA microarrays. They are based on iterative local regression and optimization of model parameters by generalized cross-validation. Permutation tests assessing the efficiency of normalization demonstrated that the proposed schemes have

Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation

by Yee Hwa Yang, Sandrine Dudoit, Percy Luu, Vivian Peng , 2002
"... There are many sources of systematic variation in cDNA microarray experiments which affect the measured gene expression levels (e.g. differences in labeling efficiency between the two fluorescent dyes). The term normalization refers to the process of removing such variation. A constant adjustment is ..."
Abstract - Cited by 718 (9 self) - Add to MetaCart
There are many sources of systematic variation in cDNA microarray experiments which affect the measured gene expression levels (e.g. differences in labeling efficiency between the two fluorescent dyes). The term normalization refers to the process of removing such variation. A constant adjustment

Limma: linear models for microarray data

by Gordon K. Smyth, Matthew Ritchie, Natalie Thorne, James Wettenhall, Wei Shi - Bioinformatics and Computational Biology Solutions using R and Bioconductor , 2005
"... This free open-source software implements academic research by the authors and co-workers. If you use it, please support the project by citing the appropriate journal articles listed in Section 2.1.Contents ..."
Abstract - Cited by 774 (13 self) - Add to MetaCart
This free open-source software implements academic research by the authors and co-workers. If you use it, please support the project by citing the appropriate journal articles listed in Section 2.1.Contents

Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments

by Sandrine Dudoit, Yee Hwa Yang, Matthew J. Callow, Terence P. Speed - STATISTICA SINICA , 2002
"... DNA microarrays are a new and promising biotechnology whichallows the monitoring of expression levels in cells for thousands of genes simultaneously. The present paper describes statistical methods for the identification of differentially expressed genes in replicated cDNA microarray experiments. A ..."
Abstract - Cited by 438 (12 self) - Add to MetaCart
DNA microarrays are a new and promising biotechnology whichallows the monitoring of expression levels in cells for thousands of genes simultaneously. The present paper describes statistical methods for the identification of differentially expressed genes in replicated cDNA microarray experiments

Comparison of discrimination methods for the classification of tumors using gene expression data

by Sandrine Dudoit, Jane Fridlyand, Terence P. Speed - JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION , 2002
"... A reliable and precise classification of tumors is essential for successful diagnosis and treatment of cancer. cDNA microarrays and high-density oligonucleotide chips are novel biotechnologies increasingly used in cancer research. By allowing the monitoring of expression levels in cells for thousand ..."
Abstract - Cited by 770 (6 self) - Add to MetaCart
A reliable and precise classification of tumors is essential for successful diagnosis and treatment of cancer. cDNA microarrays and high-density oligonucleotide chips are novel biotechnologies increasingly used in cancer research. By allowing the monitoring of expression levels in cells

Empirical Bayes Analysis of a Microarray Experiment

by Bradley Efron, Robert Tibshirani, John D. Storey, Virginia Tusher - Journal of the American Statistical Association , 2001
"... Microarrays are a novel technology that facilitates the simultaneous measurement of thousands of gene expression levels. A typical microarray experiment can produce millions of data points, raising serious problems of data reduction, and simultaneous inference. We consider one such experiment in whi ..."
Abstract - Cited by 492 (20 self) - Add to MetaCart
Microarrays are a novel technology that facilitates the simultaneous measurement of thousands of gene expression levels. A typical microarray experiment can produce millions of data points, raising serious problems of data reduction, and simultaneous inference. We consider one such experiment

Knowledge-based Analysis of Microarray Gene Expression Data By Using Support Vector Machines

by Michael P. S. Brown, William Noble Grundy, David Lin, Nello Cristianini, Charles Walsh Sugnet, Terrence S. Furey, Manuel Ares, Jr., David Haussler , 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). SVMs are considered a supervised computer learning method because they exploit prior knowledge of ..."
Abstract - Cited by 520 (8 self) - Add to MetaCart
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). SVMs are considered a supervised computer learning method because they exploit prior knowledge

Support Vector Machine Classification and Validation of Cancer Tissue Samples Using Microarray Expression Data

by Terrence S. Furey, Nello Cristianini, Nigel Duffy, David W. Bednarski, Michèl Schummer, David Haussler , 2000
"... Motivation: DNA microarray experiments generating thousands of gene expression measurements, are being used to gather information from tissue and cell samples regarding gene expression differences that will be useful in diagnosing disease. We have developed a new method to analyse this kind of data ..."
Abstract - Cited by 569 (1 self) - Add to MetaCart
Motivation: DNA microarray experiments generating thousands of gene expression measurements, are being used to gather information from tissue and cell samples regarding gene expression differences that will be useful in diagnosing disease. We have developed a new method to analyse this kind of data

Linear models and empirical bayes methods for assessing differential expression in microarray experiments.

by Gordon K Smyth , Gordon K Smyth - Stat. Appl. Genet. Mol. Biol. , 2004
"... Abstract The problem of identifying differentially expressed genes in designed microarray experiments is considered. Lonnstedt and Speed (2002) derived an expression for the posterior odds of differential expression in a replicated two-color experiment using a simple hierarchical parametric model. ..."
Abstract - Cited by 1321 (24 self) - Add to MetaCart
of interest. The approach applies equally well to both single channel and two color microarray experiments. Consistent, closed form estimators are derived for the hyperparameters in the model. The estimators proposed have robust behavior even for small numbers of arrays and allow for incomplete data arising

Missing value estimation methods for DNA microarrays

by Olga Troyanskaya, Michael Cantor, Gavin Sherlock, Pat Brown, Trevor Hastie, Robert Tibshirani, David Botstein, Russ B. Altman , 2001
"... Motivation: Gene expression microarray experiments can generate data sets with multiple missing expression values. Unfortunately, many algorithms for gene expression analysis require a complete matrix of gene array values as input. For example, methods such as hierarchical clustering and K-means clu ..."
Abstract - Cited by 477 (24 self) - Add to MetaCart
Motivation: Gene expression microarray experiments can generate data sets with multiple missing expression values. Unfortunately, many algorithms for gene expression analysis require a complete matrix of gene array values as input. For example, methods such as hierarchical clustering and K
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