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» Classification of microarray data using gene networks
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AIIA
2009
Springer
15 years 10 months ago
Ontology-Driven Co-clustering of Gene Expression Data
Abstract. The huge volume of gene expression data produced by microarrays and other high-throughput techniques has encouraged the development of new computational techniques to eva...
Francesca Cordero, Ruggero G. Pensa, Alessia Visco...
BIBE
2004
IEEE
107views Bioinformatics» more  BIBE 2004»
15 years 7 months ago
Enhanced pClustering and Its Applications to Gene Expression Data
Clustering has been one of the most popular methods to discover useful biological insights from DNA microarray. An interesting paradigm is simultaneous clustering of both genes an...
Sungroh Yoon, Christine Nardini, Luca Benini, Giov...
BMCBI
2008
107views more  BMCBI 2008»
15 years 4 months ago
A mixture model approach to sample size estimation in two-sample comparative microarray experiments
Background: Choosing the appropriate sample size is an important step in the design of a microarray experiment, and recently methods have been proposed that estimate sample sizes ...
Tommy S. Jørstad, Herman Midelfart, Atle M....
BMCBI
2007
120views more  BMCBI 2007»
15 years 4 months ago
Re-sampling strategy to improve the estimation of number of null hypotheses in FDR control under strong correlation structures
Background: When conducting multiple hypothesis tests, it is important to control the number of false positives, or the False Discovery Rate (FDR). However, there is a tradeoff be...
Xin Lu, David L. Perkins
ISMB
2000
15 years 5 months ago
Pattern Recognition of Genomic Features with Microarrays: Site Typing of Mycobacterium Tuberculosis Strains
Mycobacterium tuberculosis (M. tb.) strains differ in the number and locations of a transposon-like insertion sequence known as IS6110. Accurate detection of this sequence can be ...
Soumya Raychaudhuri, Joshua M. Stuart, Xuemin Liu,...