I am new to R and machine learning. I want to create a machine learning classifier which can classify between Normal and diseased sample using Differentially expressed genes obtained from GEO microarray datasets, as input features. I have obtained my DEGs using limma package. Now how to use DEGs to train the machine learning classifier ? plz help
You can probably do this using a something simple like a logistic regression classifier. Try searching for "logistic regression in R". Remeber that doing good ML is about more than just picking the correct algorithm. You have to carefully design training, validation and test sets, or use k-fold validation, and think carefully about what metrics you use to assess the performance of your model, particularly if you have unbalanced classes. Finally, you will ideally want a test test that comes from a different experiment this will ensure that your model is generalization. This means you'll need to think carefully about how to normalize the input data so that it is comparable across studies.
Personally, I found Andrew Ng's Coursea course on machine learning very useful to get to grips with the basic concepts in machine learning. It focuses on the surrounding concepts as much the algorithms/models themselves, which I found to be very helpful.