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GEPAS: a webbased resource for microarray gene expression data analysis

by Javier Herrero, Fátima Al-shahrour, Ramón Díaz-uriarte, Álvaro Mateos, Juan M. Vaquerizas, Javier Santoyo, Joaquín Dopazo - Nucleic Acids Res , 2003
"... We present a web-based pipeline for microarray gene expression profile analysis, GEPAS, which stands for Gene Expression Profile Analysis Suite ..."
Abstract - Cited by 67 (33 self) - Add to MetaCart
We present a web-based pipeline for microarray gene expression profile analysis, GEPAS, which stands for Gene Expression Profile Analysis Suite

Functional discovery via a compendium of expression profiles.

by Timothy R Hughes , Matthew J Marton , Allan R Jones , Christopher J Roberts , Roland Stoughton , Christopher D Armour , Holly A Bennett , Ernest Coffey , Hongyue Dai , Ross-Macdonald , Yudong D He , Matthew J Kidd , Amy M King , Michael R Meyer , David Slade , Pek Y Lum , Sergey B Stepaniants , Daniel D Shoemaker , Julian Simon , Martin Bard - Cell, , 2000
"... provided that the cellular transcriptional response to frames encode proteins required for sterol metabodisruption of different steps in the same pathway is lism, cell wall function, mitochondrial respiration, or similar, and that there are sufficiently unique transcripprotein synthesis. We also sh ..."
Abstract - Cited by 547 (9 self) - Add to MetaCart
dyclonine. sion measurement. Using a comprehensive database of reference profiles, the pathway(s) perturbed by an Introduction uncharacterized mutation would be ascertained by simply asking which expression patterns in the database Systematic approaches for identifying the biological its profile most

Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling.

by Ash A Alizadeh , Michael B Eisen , R Eric Davis , Chi Ma , Izidore S Lossos , Andreas Rosenwald , Jennifer C Boldrick , Hajeer Sabet , Truc Tran , Xin Yu , John I Powell , Liming Yang , Gerald E Marti , Troy Moore , James Hudson Jr , Lisheng Lu , David B Lewis , Robert Tibshirani , Gavin Sherlock , Wing C Chan , Timothy C Greiner , Dennis D Weisenburger , James O Armitage , Roger Warnke , Ronald Levy , Wyndham Wilson , Michael R Grever , John C Byrd , David Botstein , Patrick O Brown , Louis M Staudt - Nature , 2000
"... ..."
Abstract - Cited by 642 (10 self) - Add to MetaCart
Abstract not found

Cluster analysis and display of genome-wide expression patterns’,

by Michael B Eisen , Paul T Spellman , Patrick O Brown , David Botstein - Proc. Natl. Acad. , 1998
"... ABSTRACT 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. The output is displayed graphically, conveying the clustering and th ..."
Abstract - Cited by 2895 (44 self) - Add to MetaCart
ABSTRACT 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. The output is displayed graphically, conveying the clustering

Analysis of relative gene expression data using real-time quantitative

by Kenneth J. Livak, Thomas D. Schmittgen - PCR and 2 ���CT method. Methods 25 , 2001
"... of the target gene relative to some reference group The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantifica-such as an untreated control or a sample at time zero tion and relative quantification. Absolute quantification deter- in a time ..."
Abstract - Cited by 2666 (6 self) - Add to MetaCart
variations of the 2 ���CT method that may be script copy number and reporting the relative change useful in the analysis of real-time, quantitative PCR data. � 2001 in gene expression will suffice. For example, stating

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

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
analysis, and classification trees. Recent machine learning approaches, such as bagging and boosting, are also considered. The discrimination methods are applied to datasets from three recently published cancer gene expression studies.

A Bayesian Framework for the Analysis of Microarray Expression Data: Regularized t-Test and Statistical Inferences of Gene Changes

by Pierre Baldi, Anthony D. Long - Bioinformatics , 2001
"... Motivation: DNA microarrays are now capable of providing genome-wide patterns of gene expression across many different conditions. The first level of analysis of these patterns requires determining whether observed differences in expression are significant or not. Current methods are unsatisfactory ..."
Abstract - Cited by 491 (6 self) - Add to MetaCart
Motivation: DNA microarrays are now capable of providing genome-wide patterns of gene expression across many different conditions. The first level of analysis of these patterns requires determining whether observed differences in expression are significant or not. Current methods are unsatisfactory

Model-Based Analysis of Oligonucleotide Arrays: Model Validation, Design Issues and Standard Error Application

by Cheng Li, Wing Hung Wong , 2001
"... Background: A model-based analysis of oligonucleotide expression arrays we developed previously uses a probe-sensitivity index to capture the response characteristic of a specific probe pair and calculates model-based expression indexes (MBEI). MBEI has standard error attached to it as a measure of ..."
Abstract - Cited by 775 (28 self) - Add to MetaCart
Background: A model-based analysis of oligonucleotide expression arrays we developed previously uses a probe-sensitivity index to capture the response characteristic of a specific probe pair and calculates model-based expression indexes (MBEI). MBEI has standard error attached to it as a measure

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
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