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41,114
GEPAS: a webbased resource for microarray gene expression data analysis
- 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
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Cited by 67 (33 self)
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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.
- 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
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Cited by 547 (9 self)
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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.
- Nature
, 2000
"... ..."
Cluster analysis and display of genome-wide expression patterns’,
- 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 ..."
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Cited by 2895 (44 self)
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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
- 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
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Cited by 2666 (6 self)
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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
, 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 ..."
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Cited by 520 (8 self)
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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
- 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 ..."
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Cited by 770 (6 self)
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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
- 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
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Cited by 491 (6 self)
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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
, 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 ..."
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Cited by 775 (28 self)
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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
, 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 ..."
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Cited by 569 (1 self)
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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
Results 1 - 10
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