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- W3035662022 abstract "This chapter focuses on using deep learning (DL) to infer on selection on a whole-genome variation data of Drosophila melanogaster species. A maximum likelihood-based approach to identify events inducing low genetic variability and differentiating between demographic bottlenecks and positive selection is described by Galtier et al. over African population data of D.melanogaster. For the selection, the simulation procedure followed by Y. S. Song and S. Sheehan and Peter et al. as well as the simulation data provided is used. The chapter helps the reader to first decide upon a classification algorithm that is best suited to problem. Because there are multiple machine learning algorithms and DL architectures to choose from and each of their implementation is time consuming, the automatic machine learning (automl) framework is used to choose the best classification algorithm for the task." @default.
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- W3035662022 date "2020-06-12" @default.
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- W3035662022 title "Deep Learning in Population Genetics" @default.
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