Code from: Negative frequency-dependent selection: A positive outlook with deep learning

Balancing selection is a mode of natural selection that maintains genetic
diversity through an array of mechanisms, including negative
frequency-dependent selection. However, discriminating genomic footprints
of negative frequency-dependent selection from those of other forms of
balancing selection mechanisms is a difficult task. In this perspective,
we will present directions on how to enhance the modeling of genomic
signals expected from negative frequency-dependent selection to better
distinguish it from neutrality and other forms of balancing selection,
such as overdominance. Specifically, we demonstrate how deep learning can
facilitate detection and characterization of this process through novel
data preprocessing and modeling of genomic and temporal autocovariation.
We also provide a series of recommendations to empiricists and method
developers on how to positively approach the problem of identifying
genomic footprints of negative frequency-dependent selection in the
future.