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BMCBI
2010
172views more  BMCBI 2010»
13 years 7 months ago
Inferring gene regression networks with model trees
Background: Novel strategies are required in order to handle the huge amount of data produced by microarray technologies. To infer gene regulatory networks, the first step is to f...
Isabel A. Nepomuceno-Chamorro, Jesús S. Agu...
BMCBI
2007
97views more  BMCBI 2007»
13 years 7 months ago
In situ analysis of cross-hybridisation on microarrays and the inference of expression correlation
Background: Microarray co-expression signatures are an important tool for studying gene function and relations between genes. In addition to genuine biological co-expression, corr...
Tineke Casneuf, Yves Van de Peer, Wolfgang Huber
CIBCB
2005
IEEE
14 years 1 months ago
Feature Selection for Microarray Data Using Least Squares SVM and Particle Swarm Optimization
Feature selection is an important preprocessing technique for many pattern recognition problems. When the number of features is very large while the number of samples is relatively...
E. Ke Tang, Ponnuthurai N. Suganthan, Xin Yao
BIOINFORMATICS
2006
124views more  BIOINFORMATICS 2006»
13 years 7 months ago
Probabilistic inference of transcription factor concentrations and gene-specific regulatory activities
Motivation Quantitative estimation of the regulatory relationship between transcription factors and genes is a fundamental stepping stone when trying to develop models of cellular...
Guido Sanguinetti, Neil D. Lawrence, Magnus Rattra...
BMCBI
2006
119views more  BMCBI 2006»
13 years 7 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