[BioC] Classification

[Kevin R. Coombes]

.. and probably should ...

For a binary classification with only a few predictors, you can, for 
example, use logistic regression with some standard criterion like AIC, 
BIC, or Bayesian model averaging to decide which predictors should be 
retained.

     Kevin

On 6/23/2011 6:10 PM, Moshe Olshansky wrote:
> If you have just 5 genes and a decent number of samples you can use any of
> the "conventional" (i.e. not high throughput) methods like LDA, trees,
> Random Forest, SVM, etc.
>
>> I will have a look at both packages. It's pcr data by the way
>> thanks
>>
>> On 06/23/2011 05:56 PM, Tim Triche, Jr. wrote:
>>> or CMA, which is perhaps a more systematic approach for classification.
>>>    (the package name stands for Classification of MicroArrays)  Very well
>>> thought out.
>>>
>>>
>>> On Thu, Jun 23, 2011 at 8:02 AM, Sean Davis<sdavis2 at mail.nih.gov>
>>> wrote:
>>>
>>>> On Thu, Jun 23, 2011 at 10:58 AM, David martin<vilanew at gmail.com>
>>>> wrote:
>>>>> Hi,
>>>>> I have 5 genes of interest. I would like to know which combination(s)
>>>>> of
>>>>> genes gives the best disease separation. Which test could i use in my
>>>>> training set to see which combination is the best classificer between
>>>>> my
>>>>> disease and my healthy population.
>>>>>
>>>>> Thanks for any comment or test that could be useful to answer that
>>>> question.
>>>>
>>>> Check out the MLInterfaces package.  It should give you some ideas on
>>>> where to start.
>>>>
>>>> Sean
>>>>
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>>>
>>>
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>
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