Proper feature selection methods for classifying signal into two category
Generally, feature selection is somewhat of a fuzzy process. Since you usually don't have a ground truth in predicting biology, you will always have to consider how realistic whatever you came up with is. I would recommend to start with the most simple method and see how your model performs. After establishing this as a baseline, you can then explore other feature selection methods to see if that improves your model.
For most models, there are general assumptions that should be met - check this before trying to optimise your features. So, for example, if one feature is just the sum of featureA and featureB, you don't need it in your data. Also, of course, always be cautious about over/under-fitting and do proper cross-validation. And if you come up with a set of features, it might make sense to think about their implications since ML is prone to find shortcuts that are only specific to your data.