# Difference in Feature selection methods between classification and regression problems?

For high-dimensional molecular genetic data, is there a difference in available feature selection techniques between classification problems and regression problems? Or can all feature selection techniques be applied to either classification and regression modeling indiscriminately?

There is a huge difference. Classification has the efficiency of the sign test at best ($\frac{2}{\pi}$) whereas prediction can use all the information in the data and will work better on new samples. When using classification, the entire classification scheme may have to be re-done from scratch if you alter the outcome prevalence through oversampling.