The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Analyzing gene expression data in terms of gene sets
Bioinformatics
Set-level analyses for genome-wide association data
ISBRA'08 Proceedings of the 4th international conference on Bioinformatics research and applications
Comparative evaluation of set-level techniques in microarray classification
ISBRA'11 Proceedings of the 7th international conference on Bioinformatics research and applications
Gaussian logic for predictive classification
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Gene Set Cultural Algorithm: A Cultural Algorithm Approach to Reconstruct Networks from Gene Sets
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
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We demonstrate a set-level approach to the integration of multiple platform gene expression data for predictive classification and show its utility for boosting classification performance when single- platform samples are rare. We explore three ways of defining gene sets, including a novel way based on the notion of a fully coupled flux related to metabolic pathways. In two tissue classification tasks, we empirically show that the gene set based approach is useful for combining heterogeneous expression data, while surprisingly, in experiments constrained to a single platform, biologically meaningful gene sets acting as sample features are often outperformed by random gene sets with no biological relevance.