Conference Proceeding

M@CBETH: Optimizing Clinical Microarray Classification.

Dept. of Electr. Eng., Katholieke Univ., Leuven, Belgium
01/2005; DOI:10.1109/CSBW.2005.86 In proceeding of: Fourth International IEEE Computer Society Computational Systems Bioinformatics Conference Workshops & Poster Abstracts (CSB 2005 Workshops), 8-11 August 2005, Stanford, CA, USA
Source: DBLP

ABSTRACT The M@CBETH (microarray classification bench-marking tool on host server) web service, available at http://www.esat.kuleuven.be/MACBETH/, offers a simple tool for making optimal two-class predictions in a clinical setting. This web service compares different classifiers and selects the best in terms of randomized test set performances. The M@CBETH website offers two services: benchmarking and prediction. Benchmarking involves selection and training of an optimal model based on a benchmarking dataset. This model is stored for immediate or later use on prospective data. The prediction service offers a way for later evaluation of prospective data by reusing an existing optimal prediction model, which is useful for classifying new unseen patients. Nine different classification methods are considered. Application of the M@CBETH benchmarking service on two binary classification problems in ovarian cancer confirms that it is important to select and train an optimal model for each microarray dataset.

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    ABSTRACT: Microarray classification can be useful to support clinical management decisions for individual patients in, for example, oncology. However, comparing classifiers and selecting the best for each microarray dataset can be a tedious and non-straightforward task. The M@CBETH (a MicroArray Classification BEnchmarking Tool on a Host server) web service offers the microarray community a simple tool for making optimal two-class predictions. M@CBETH aims at finding the best prediction among different classification methods by using randomizations of the benchmarking dataset. The M@CBETH web service intends to introduce an optimal use of clinical microarray data classification.
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