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Microarray Gene Expression Data Analysis

by George Vachtsevanos, Yuhua Ding, Jacqueline A. Fairley, Andrew B. Gardner, Petia Simeonova
"... Image analysis is a crucial step in processing microarray data generated by gene expression studies, which have been used extensively in understanding the molecular mechanisms of injury and recovery. A novel image analysis method utilizing an efficient snake-based multichannel image segmentation alg ..."
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Image analysis is a crucial step in processing microarray data generated by gene expression studies, which have been used extensively in understanding the molecular mechanisms of injury and recovery. A novel image analysis method utilizing an efficient snake-based multichannel image segmentation

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

MICROARRAY GENE EXPRESSION DATA ANALYSIS USING ENHANCED K-MEANS CLUSTERING METHOD

by Muhammad Rukunuddin Ghalib, Rittwika Ghosh, Priti Sasmal, Udisha P
"... This Clustering analysis method is one of the important methods which can influence clustering results directly. Among all the clustering methods, k-means clustering is one of the most popular schemes owing to its simplicity and practicality. In this paper we’ve discussed the standard clustering alg ..."
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of gene expression data generated using microarrays. This type of experiment allows determining relative levels of mRNA abundance in a set of tissues or cell populations for thousands of genes simultaneously. We have proposed and implemented an enhanced k-means algorithm which stabilizes and thereby

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by Fátima Al-shahrour, Juan M Vaquerizas, Javier Santoyo-lopez, Joaquin Dopazo, Centro Investigación, Príncipe Felipe, Javier Herrero, Juan M. Vaquerizas, Javier Santoyo , 2003
"... GEPAS: A web-based resource for microarray gene expression data analysis ..."
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GEPAS: A web-based resource for microarray gene expression data analysis

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

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

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

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

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

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
gene expression data is an important aspect of this novel approach to cancer classification. This article compares the performance of different discrimination methods for the classification of tumors based on gene expression data. The methods include nearest-neighbor classifiers, linear discriminant
Results 1 - 10 of 64,132