Feature Selection algorithm for a high featured data I have a cancer patient database from mass spectrometry on patients which consists of more than half million features. My task is to apply a feature selection algorithm to extract the most relevant features from it. My question is, which feature selection model would be the most appropriate in this case? Any suggestion from practical experience for these types of data is appreciated.
Have you tried any possibilities?

You have many possible approaches.

FeatureMiner (http://featureselection.asu.edu/index.php) or SkLearn (https://scikit-learn.org/) are popular options for your purposes. 

The documentation from these two possibilities is also interesting.