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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

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

Discovering Statistically Significant Biclusters in Gene Expression Data

by Amos Tanay, Roded Sharan, Ron Shamir - In Proceedings of ISMB 2002 , 2002
"... In gene expression data, a bicluster is a subset of the genes exhibiting consistent patterns over a subset of the conditions. We propose a new method to detect significant biclusters in large expression datasets. Our approach is graph theoretic coupled with statistical modelling of the data. Under p ..."
Abstract - Cited by 302 (4 self) - Add to MetaCart
In gene expression data, a bicluster is a subset of the genes exhibiting consistent patterns over a subset of the conditions. We propose a new method to detect significant biclusters in large expression datasets. Our approach is graph theoretic coupled with statistical modelling of the data. Under

Gene expression data analysis

by Alvis Brazma, Jaak Vilo - FEBS Lett , 2000
"... Abstract Microarrays are one of the latest breakthroughs in experimental molecular biology, which allow monitoring of gene expression for tens of thousands of genes in parallel and are already producing huge amounts of valuable data. Analysis and handling of such data is becoming one of the major bo ..."
Abstract - Cited by 144 (1 self) - Add to MetaCart
Abstract Microarrays are one of the latest breakthroughs in experimental molecular biology, which allow monitoring of gene expression for tens of thousands of genes in parallel and are already producing huge amounts of valuable data. Analysis and handling of such data is becoming one of the major

Validating Clustering for Gene Expression Data

by Ka Yee Yeung, Ka Yee Yeung, David R. Haynor, David R. Haynor, Walter L. Ruzzo, Walter L. Ruzzo - Bioinformatics , 2000
"... Many clustering algorithms have been proposed to analyze gene expression data, but little guidance is available to help choose among them. We provide a systematic and quantitative framework to assess the results of clustering algorithms. A typical gene expression data set contains measurements of ..."
Abstract - Cited by 129 (5 self) - Add to MetaCart
Many clustering algorithms have been proposed to analyze gene expression data, but little guidance is available to help choose among them. We provide a systematic and quantitative framework to assess the results of clustering algorithms. A typical gene expression data set contains measurements

GEPAS, an experiment-oriented pipeline for the analysis of microarray gene expression data

by Juan M. Vaquerizas, Lucía Conde, Patricio Yankilevich, Amaya Cabezón, Pablo Minguez, Ramón Díaz-uriarte, Fátima Al-shahrour, Javier Herrero, Joaquín Dopazo - Nucleic Acids Res , 2005
"... gene expression data ..."
Abstract - Cited by 30 (11 self) - Add to MetaCart
gene expression data

Co-Clustering of Biological Networks and Gene Expression Data

by Daniel Hanisch, Alexander Zien, Ralf Zimmer, Thomas Lengauer, Thomas , 2002
"... Motivation: Large scale gene expression data are often analyzed by clustering genes based on gene expression data alone, though a-priori knowledge in the form of biological networks is available. The use of this additional information promises to improve exploratory analysis considerably. ..."
Abstract - Cited by 120 (6 self) - Add to MetaCart
Motivation: Large scale gene expression data are often analyzed by clustering genes based on gene expression data alone, though a-priori knowledge in the form of biological networks is available. The use of this additional information promises to improve exploratory analysis considerably.

BagBoosting for tumor classification with gene expression data

by Marcel Dettling - Bioinformatics , 2004
"... Motivation: Microarray experiments are expected to contribute significantly to the progress in cancer treatment by enabling a precise and early diagnosis. They create a need for class prediction tools, which can deal with a large number of highly correlated input variables, perform feature selection ..."
Abstract - Cited by 194 (2 self) - Add to MetaCart
classifier consistently improves the predictive performance and the probability estimates of both bagging and boosting on real and simulated gene expression data. This quasi-guaranteed improvement can be obtained by simply making a bigger computing effort. The advantageous predictive potential is also

Plaid Models for Gene Expression Data

by Laura Lazzeroni, Art Owen - Statistica Sinica , 2000
"... Motivated by genetic expression data, we introduce plaid models. These are a form of two-sided cluster analysis that allows clusters to overlap. Using these models we nd interpretable structure in some yeast DNA data, as well as in some nutrition data and some foreign exchange data. Key Words: ..."
Abstract - Cited by 169 (0 self) - Add to MetaCart
: clustering, DNA microarray, information retrieval 1 Introduction This article introduces the plaid model, a tool for exploratory analysis of multivariate data. The motivating application is the search for interpretable biological structure in gene expression microarray data. Eisen, Spellman, Brown &
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