Abstract
In this chapter, supervised machine learning methods are described in the context of microarray applications. The most widely used families of machine learning methods are described, along with various approaches to learner assessment. The Bioconductor interfaces to machine learning tools are described and illustrated. Key problems of model selection and interpretation are reviewed in examples.
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© 2005 Springer Science+Business Media, Inc.
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Carey, V.J. (2005). Machine Learning Concepts and Tools for Statistical Genomics. In: Gentleman, R., Carey, V.J., Huber, W., Irizarry, R.A., Dudoit, S. (eds) Bioinformatics and Computational Biology Solutions Using R and Bioconductor. Statistics for Biology and Health. Springer, New York, NY. https://doi.org/10.1007/0-387-29362-0_16
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DOI: https://doi.org/10.1007/0-387-29362-0_16
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-25146-2
Online ISBN: 978-0-387-29362-2
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