Methodological Review: Towards knowledge-based gene expression data mining
Journal of Biomedical Informatics
Techniques for clustering gene expression data
Computers in Biology and Medicine
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ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
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IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Clustering of multiple microarray experiments using information integration
ITBAM'11 Proceedings of the Second international conference on Information technology in bio- and medical informatics
Expert Systems with Applications: An International Journal
Intelligent Data Analysis - Combined Learning Methods and Mining Complex Data
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In this paper we consider a general framework for clustering expression data that permits integration of various biological data sources through combination of corresponding dissimilarity measures. In the paper we briefly review currently published attempts to genomic data fusion and discuss a problem of validating results from clustering expression data. We apply our approach to a real microarray expression dataset which induces a correlationbased dissimilarity matrix, and use Gene Ontology - Biological Process annotations to derive GO-based dissimilarity matrix. The proposed procedure is verified using a simple knowledge-based validation measure based on protein-protein interaction database. Obtained results reveal that combining experimental data with comprehensive and reliable biological repository may improve performance of cluster analysis and yield biologically meaningful gene clusters.