Feature selection for gene expression dataset
It would be helpful if you described your dataset more.  Gene expression datasets seem to often have very high dimensionality and Lasso regularized logistic regression is a popular method to approach this problem.  This paper takes it a little further and might help you out:
http://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-14-198

Random forest can generally certainly provide a meaningful importance ranking, but it also depends on what your dataset looks like.

mRMR sounds like it is specifically designed for identifying gene characteristics, so definitely give it a try.

There's also Principle Component Analysis which is also used for gene expression data.

Lots of options, but your questions is not detailed enough to go any further, and providing code as a solution at this point isn't realistic. The documentation for Python scikit-learn has many good explanations and examples.