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» Clustering cancer gene expression data: a comparative study
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BMCBI
2006
144views more  BMCBI 2006»
15 years 6 months ago
More robust detection of motifs in coexpressed genes by using phylogenetic information
Background: Several motif detection algorithms have been developed to discover overrepresented motifs in sets of coexpressed genes. However, in a noisy gene list, the number of ge...
Pieter Monsieurs, Gert Thijs, Abeer A. Fadda, Sigr...
BCB
2010
138views Bioinformatics» more  BCB 2010»
15 years 1 months ago
Comparative analysis of biclustering algorithms
Biclustering is a very popular method to identify hidden co-regulation patterns among genes. There are numerous biclustering algorithms designed to undertake this challenging task...
Doruk Bozdag, Ashwin S. Kumar, Ümit V. &Ccedi...
BIBE
2007
IEEE
151views Bioinformatics» more  BIBE 2007»
15 years 8 months ago
On the Effectiveness of Constraints Sets in Clustering Genes
—In this paper, we have modified a constrained clustering algorithm to perform exploratory analysis on gene expression data using prior knowledge presented in the form of constr...
Erliang Zeng, Chengyong Yang, Tao Li, Giri Narasim...
BMCBI
2007
97views more  BMCBI 2007»
15 years 6 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
GECCO
2007
Springer
197views Optimization» more  GECCO 2007»
16 years 10 days ago
Computational intelligence techniques: a study of scleroderma skin disease
This paper presents an analysis of microarray gene expression data from patients with and without scleroderma skin disease using computational intelligence and visual data mining ...
Julio J. Valdés, Alan J. Barton