[BioC] Re: Classification question(Tom R. Fahland)
Tarca Adi Laurentiu
ltarca at rsvs.ulaval.ca
Fri Apr 30 16:04:52 CEST 2004
>>Date: Thu, 1 Apr 2004 15:47:48 -0800
>>From: "Tom R. Fahland" <tfahland at genomatica.com>
>>Subject: [BioC] Classification question
>>I had a quick question about how you might best solve a classification
>>problem. I have some ideas, but wanted to run it by the group to see their
>>thoughts. I have animal data containing different doses of a substance
>>have multiple time points for each dose (with replicates). I am
>>classifying the samples based on dose amount. I am experimenting with
>>techniques like neural nets, etc. Now this problem is striaght forward
if you have only one
>>time point per dose, just group similar doses together and train the
>>network. But its alittle more tricky with multiple time points. What do
>>you think is the best way to fully utilize all the data for dosage
>>classification. How would you use/incorporate the mulitple time points?
If I understand well, there are C levels of dose (predefined classes) in
which your hybridizations fall.
Then, perhaps you consider only a reduced set of (most regulated) say Ng
genes (but always the same) and want to use their (normalized) M values at
the Nt different time points to predict the class.
So your samples my be viewed as NgxNt matrices of features you dispose to
perform the classification and your problem is mostly how to reduce the
numbers of features.
There are mainly two types of dimensionality reduction methods: feature
extraction and feature selection.
You may perform feature extraction with for e.g. Principal Component
Analysis so you may reduce the Nt dimensions to lets say only 2 (the first
two principal components) of your data, but you will still have Ngx2
features to input into your classifier.
With feature selection you may select among all NgxNt those feature that
are the most "relevant" for classification without altering their meaning
(as PCA does).
I may provide you with a matalb implementation of a feature selector
algorithm which uses as relevance measure the n-fold cross-validated
accuracy of a nearest neighbor classifier and as combinatorial optimization
algorithm (maximizing the relevance) a sequential method like sequential
forward selection or "plus l take away r". As the number of samples you
have is reduced I believe it will work fine for Ng=20xNt=10 features, or
Once the features are selected you may use them with any supervised
Dr. Laurentiu Adi Tarca
Post Doc. in Bioinformatics
Forest Biology Research Center
C-E-Marchand Bld, 3113
Tel: 656-2131 ext. 4509
e-mail: ltarca at rsvs.ulaval.ca
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