An ensemble approach for phenotype classification based on fuzzy partitioning of gene expression data.
We focus on developing a pattern recognition method suitable for performing supervised analysis tasks on molecular data resulting from microarray experiments. Molecular characterization of tissue samples using microarray gene expression profiling is expected to uncover fundamental aspects related to cancer diagnosis and drug discovery. There is therefore a need for reliable, accurate classification methods. With this study we propose a framework for constructing an ensemble of individually trained SVM classifiers, each of them specialized on subsets of the input space. The fuzzy approach used for partitioning the data produces overlapping subsets of the input space that facilitates subsequent classification tasks.
Dragomir
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Dept. of Med. Phys., Patras Univ., Rio, Greece. adragomir@heart.med
Maraziotis
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Bezerianos
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D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D015195:Drug Design; D017143:Fuzzy Logic; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D020407:Internet; D015233:Models, Statistical; D008962:Models, Theoretical; D009369:Neoplasms / Q000175:diagnosis / Q000235:genetics* / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis; D010363:Pattern Recognition, Automated; D010641:Phenotype