Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
Protein function prediction via graph kernels
Bioinformatics
Microarray data analysis with PCA in a DBMS
Proceedings of the 2nd international workshop on Data and text mining in bioinformatics
A cube framework for incorporating inter-gene information into biological data mining
International Journal of Data Mining and Bioinformatics
An adaptive approach for integration analysis of multiple gene expression datasets
AIMSA'10 Proceedings of the 14th international conference on Artificial intelligence: methodology, systems, and applications
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Microarray data integration is an important yet challenging problem. Usually, direct integration of microarrays after normalization is ineffective because of the diverse types of experiment specific variations. To address this issue, two novel integration approaches were proposed in recent microarray studies. The first study[16] presented a cancer classification technique which identifies gene pairs whose expression orders are consistent within class and different across classes. The other study[18] presented a promising gene expression analysis technique which utilizes pairwise correlations of gene expressions across different microarray datasets. Interestingly, we observe that both of the independently developed techniques rely on inter-gene information and noise filtering strategy to achieve satisfactory performance in microarray integration. Motivated by this observation, we propose in this paper a formal data model for microarray integration using inter-gene information and effective filtering, which generalizes the previous two frameworks. We also show how the proposed model can handle a broader range of problems than the previous frameworks.