Results 181 to 190 of about 188,901 (238)

Analysis techniques for microarray time-series data

Proceedings of the fifth annual international conference on Computational biology, 2001
We address possible limitations of publicly available data sets of yeast gene expression. We study the predictability of known regulators via time-series analysis, and show that less than 20% of known regulatory pairs exhibit strong correlations in the Cho/Spellman data sets.
Jizu Zhi, Steven Skiena, Vladimir Filkov
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A Survey on Filter Techniques for Feature Selection in Gene Expression Microarray Analysis

IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2012
A plenitude of feature selection (FS) methods is available in the literature, most of them rising as a need to analyze data of very high dimension, usually hundreds or thousands of variables. Such data sets are now available in various application areas like combinatorial chemistry, text mining, multivariate imaging, or bioinformatics.
Lazar, Cosmin, Taminau, Jonatan, Meganck, Stijn, Steenhoff, David, Nowe, Ann, Coletta, Alain, Molter, Colin, De Schaetzen, Virginie, Duque, Robin, Bersini, Hugues   +9 more
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Pattern Recognition Techniques in Microarray Data Analysis

Annals of the New York Academy of Sciences, 2002
Abstract:Recent development of technologies (e.g., microarray technology) that are capable of producing massive amounts of genetic data has highlighted the need for new pattern recognition techniques that can mine and discoverbiologically meaningfulknowledge in large data sets.
F. Valafar
openaire   +4 more sources

A Survey of Classification Techniques for Microarray Data Analysis

Handbook of Statistical Bioinformatics, 2011
With the recent advance of biomedical technology, a lot of ‘OMIC’ data from genomic, transcriptomic, and proteomic domain can now be collected quickly and cheaply. One such technology is the microarray technology which allows researchers to gather information on expressions of thousands of genes all at the same time.
Wai-Ki Yip, Samir B. Amin, Cheng Li
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Genome-Wide Analysis of Pancreatic Cancer Using Microarray-Based Techniques

Pancreatology, 2009
Microarray-based comparative genomic hybridisation (CGH) has allowed high-resolution analysis of DNA copy number alterations across the entire cancer genome. Recent advances in bioinformatics tools enable us to perform a robust and highly sensitive analysis of array CGH data and facilitate the discovery of novel cancer-related genes.We analysed a total
Claude Chelala, Tatjana Crnogorac-Jurcevic, Nicholas R. Lemoine, Tomohiko Harada   +3 more
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Comparative study on dimension reduction techniques for cluster analysis of microarray data

The 2011 International Joint Conference on Neural Networks, 2011
This paper proposes a study on the impact of the use of dimension reduction techniques (DRTs) in the quality of partitions produced by cluster analysis of microarray datasets. We tested seven DRTs applied to four microarray cancer datasets and ran four clustering algorithms using the original and reduced datasets. Overall results showed that using DRTs
Daniel Araújo, Adrião Duarte Dória Neto, Jorge Dantas de Melo, Allan de Medeiros Martins   +3 more
openaire   +3 more sources