Results 1 - 10 of 103,135

Clustering Gene Expression Patterns

by Amir Ben-Dor, Ron Shamir, Zohar Yakhini , 1999
"... Recent advances in biotechnology allow researchers to measure expression levels for thousands of genes simultaneously, across different conditions and over time. Analysis of data produced by such experiments offers potential insight into gene function and regulatory mechanisms. A key step in the ana ..."
Abstract - Cited by 451 (11 self) - Add to MetaCart
Recent advances in biotechnology allow researchers to measure expression levels for thousands of genes simultaneously, across different conditions and over time. Analysis of data produced by such experiments offers potential insight into gene function and regulatory mechanisms. A key step

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
to that of another transcript copy number. Absolute quantification has sample such as an untreated control. The 2���CT method is a been combined with real-time PCR and numerous reconvenient way to analyze the relative changes in gene expression ports have appeared in the literature (6–9) including from real

Molecular classification of cancer: class discovery and class prediction by gene expression monitoring

by T. R. Golub, D. K. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J. P. Mesirov, H. Coller, M. L. Loh, J. R. Downing, M. A. Caligiuri, C. D. Bloomfield - Science , 1999
"... Although cancer classification has improved over the past 30 years, there has been no general approach for identifying new cancer classes (class discovery) or for assigning tumors to known classes (class prediction). Here, a generic approach to cancer classification based on gene expression monitori ..."
Abstract - Cited by 1779 (19 self) - Add to MetaCart
Although cancer classification has improved over the past 30 years, there has been no general approach for identifying new cancer classes (class discovery) or for assigning tumors to known classes (class prediction). Here, a generic approach to cancer classification based on gene expression

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

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

Analysis of variance for gene expression microarray data

by M. Kathleen Kerr, Mitchell Martin, Gary A. Churchill - Journal of Computational Biology , 2000
"... Spotted cDNA microarrays are emerging as a powerful and cost-effective tool for largescale analysis of gene expression. Microarrays can be used to measure the relative quantities of speci � c mRNAs in two or more tissue samples for thousands of genes simultaneously. While the power of this technolog ..."
Abstract - Cited by 362 (5 self) - Add to MetaCart
Spotted cDNA microarrays are emerging as a powerful and cost-effective tool for largescale analysis of gene expression. Microarrays can be used to measure the relative quantities of speci � c mRNAs in two or more tissue samples for thousands of genes simultaneously. While the power

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

Tissue Classification with Gene Expression Profiles

by Amir Ben-Dor, Laurakay Bruhn, Agilent Laboratories, Nir Friedman, Miche`l Schummer, Iftach Nachman, U. Washington, U. Washington, Zohar Yakhini - Journal of Computational Biology , 2000
"... Constantly improving gene expression profiling technologies are expected to provide understanding and insight into cancer related cellular processes. Gene expression data is also expected to significantly aid in the development of efficient cancer diagnosis and classification platforms. In this work ..."
Abstract - Cited by 240 (11 self) - Add to MetaCart
Constantly improving gene expression profiling technologies are expected to provide understanding and insight into cancer related cellular processes. Gene expression data is also expected to significantly aid in the development of efficient cancer diagnosis and classification platforms

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
Results 1 - 10 of 103,135