[BioC] Best options for cross validation machine learning

[Joern Toedling]

Hi Dan,

one more suggestion, a few former colleagues of mine used to teach the
statistical reasoning for addressing these problems and how to solve them in R
in a very accessible way.
Have a look at the course material from that course here:
http://compdiag.molgen.mpg.de/ngfn/pma2005nov.shtml
especially Day 3: Molecular Diagnosis may of relevance for you.

Regards,
Joern

On Tue, 19 Jan 2010 16:11:14 +0000, Daniel Brewer wrote
> Hello,
> 
> 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
> 
> Dan

---
Joern Toedling
Institut Curie -- U900
26 rue d'Ulm, 75005 Paris, FRANCE
Tel. +33 (0)156246927