I'm not specifically familiar with microarray data. But what think that what you are looking to create is a regression model. (http://en.wikipedia.org/wiki/Regression_analysis) The most basic example of this being linear regression.

To reduce the number of genes used by the classifier you might want to look into feature selection (http://en.wikipedia.org/wiki/Feature_selection) - this should help you select a subset of genes.

There are a number of programs which implement general machine learning algorithms, WEKA which a alot of people seem to use. And RapidMiner, which I personally prefer. If you want a good starting place for learning rapid miner, this blog and accompanying youtube channel should give you a good start: http://vancouverdata.blogspot.se/

As I said, I have never worked with microarray data, but the methods that I mention should be transferable to any machine learning problem. Hope that this helps. :)

Johan is correct; what you have is a regression problem, not a classification problem. First, I suggest you read up on the elements of linear models. Dalgaard's book is very accessible. Then, consider looking at the Lasso, which is a selection method for linear regression (i.e. it attempts to find the smallest set of features which provide a good fit). There is a large literature here, but this is one place to start. Several libraries in R implement variants of the lasso (google "r lasso" or head over to CRAN).

my study is related to your study, i used Least Angle Regression and Lasso, but im looking for a microarray data for me to use. i need your help, i need a microarray data set. thank you!