[BioC] Best options for cross validation machine learning

Daniel Brewer daniel.brewer at icr.ac.uk
Tue Jan 19 17:11:14 CET 2010


I have a microarray dataset which I have performed an unsupervised
Bayesian clustering algorithm on which divides the samples into four
groups.  What I would like to do is:
1) Pick a group of genes that best predict which group a sample belongs to.
2) Determine how stable these prediction sets are through some sort of
cross-validation (I would prefer not to divide my set into a training
and test set for stage one)

These steps fall into the supervised machine learning realm which I am
not familiar with and googling around the options seem endless.  I was
wondering whether anyone could suggest reasonable well-established
algorithms to use for both steps.

Many thanks


Daniel Brewer, Ph.D.

Institute of Cancer Research
Molecular Carcinogenesis
Email: daniel.brewer at icr.ac.uk

The Institute of Cancer Research: Royal Cancer Hospital, a charitable Company Limited by Guarantee, Registered in England under Company No. 534147 with its Registered Office at 123 Old Brompton Road, London SW7 3RP.

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