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» Combining microarrays and genetic analysis
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BMCBI
2008
144views more  BMCBI 2008»
13 years 10 months ago
WGCNA: an R package for weighted correlation network analysis
Background: Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method ...
Peter Langfelder, Steve Horvath
BMCBI
2008
94views more  BMCBI 2008»
13 years 10 months ago
A comprehensive re-analysis of the Golden Spike data: Towards a benchmark for differential expression methods
Background: The Golden Spike data set has been used to validate a number of methods for summarizing Affymetrix data sets, sometimes with seemingly contradictory results. Much less...
Richard D. Pearson
HIS
2007
13 years 11 months ago
Genetic Programming meets Model-Driven Development
Genetic programming is known to provide good solutions for many problems like the evolution of network protocols and distributed algorithms. In such cases it is most likely a hard...
Thomas Weise, Michael Zapf, Mohammad Ullah Khan, K...
BMCBI
2006
183views more  BMCBI 2006»
13 years 10 months ago
Mining gene expression data by interpreting principal components
Background: There are many methods for analyzing microarray data that group together genes having similar patterns of expression over all conditions tested. However, in many insta...
Joseph C. Roden, Brandon W. King, Diane Trout, Ali...
BMCBI
2006
119views more  BMCBI 2006»
13 years 10 months ago
LS-NMF: A modified non-negative matrix factorization algorithm utilizing uncertainty estimates
Background: Non-negative matrix factorisation (NMF), a machine learning algorithm, has been applied to the analysis of microarray data. A key feature of NMF is the ability to iden...
Guoli Wang, Andrew V. Kossenkov, Michael F. Ochs