Deep learning in population genetics.Population genetics is transitioning into a data-driven discipline thanks to the availability of large-scale genomic data and the need to study increasingly complex evolutionary scenarios.  With likelihood and Bayesian approaches becoming either intractable or computationally unfeasible, machine learning, and in particular deep learning, algorithms are emerging as popular techniques for population genetic inferences.  Deep learning algorithms currently employed in the field comprise discriminative and generative models with fully connected, con volutional, or recurrent layers.  The application of deep learning to empirical data sets mostly replicates previous findings of demography reconstruction and signals of natural selection in model organisms.  To showcase the feasibility of deep learning to tackle new challenges, we designed a branched architecture to detect signals of recent balancing selection from temporal haplotypic data, which exhibited good predictive performance on simulated data. 
Deep learning
With likelihood and Bayesian approaches becoming either intractable or computationally unfeasible, machine learning, and in particular deep learning, algorithms are emerging as popular techniques for population genetic inferences. 
machine learning
Deep learning algorithms currently employed in the field comprise discriminative and generative models with fully connected, con volutional, or recurrent layers. 
deep learning algorithms
Deep learning algorithms currently employed in the field comprise discriminative and generative models with fully connected, con volutional, or recurrent layers. 
recurrent layers