Mosclust: A software library for discovering significant structures in bio-molecular data

Bioinformatics

Volumn 23, Issue 3, 2007, Pages 387-389

  Valentini, Giorgio ^a  

^a UNIVERSITY OF MILAN   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; BIOINFORMATICS; CHEMICAL STRUCTURE; COMPUTER PROGRAM; INFORMATION PROCESSING; PRIORITY JOURNAL; RANDOM SAMPLE; STATISTICAL SIGNIFICANCE;

EID: 33847265458     PISSN: 13674803     EISSN: 13674811     Source Type: Journal    
DOI: 10.1093/bioinformatics/btl600     Document Type: Article

References (9)

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2. Bertoni,A. and Valentini,G. (2006) Model order selection for clustered bio-molecular data. In Probabilistic Modeling and Machine Learning in Structural and Systems Biology, Tuusula, Finland, pp. 85-90.
3. Bolshakova,N. et al. (2005) An integrated tool for microarray data clustering and cluster validity assessment. Bioinformatics, 21, 451-455.
4. Golub,T.R. et al. (1999) Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science, 286, 531-537.
5. Handl,J. et al. (2005) Computational cluster validation in post-genomic data analysis. Bioinformatics, 21, 3201-3215.
6. Jain,A.K. and Dubes,R.C. (1988) Algorithms for Clustering Data. Prentice Hall, NJ.
7. McShane,L.M. et al. (2002) Method for assessing reproducibility of clustering patterns observed in analyses of microarray data. Bioinformatics, 18, 1462-1469.
8. Monti,S. et al. (2003) Consensus clustering: A resampling-based method for class discovery and visualization of gene expression microarray data. Mach. Learn., 52, 91-118.
9. Valentini,G. (2006) Clusterv: A tool for assessing the reliability of clusters discovered in DNA microarray data. Bioinformatics, 22, 369-370.