Towards automated population genetic inference using deep neural networks

This talk will focus on a novel deep learning algorithm, evoNet, that can jointly estimate parameters from genomic data such as DNA from many individuals. In population genetics, the evolutionary factors that shape variation often leave signatures that are difficult to disentangle. This makes joint inference both necessary and challenging, especially in the case of demographic history and natural selection. Deep learning automatically teases out important features of the data, which makes it useful for biological problems where the underlying models are computationally intractable and appropriate summary statistics are unknown. In particular, convolutional neural networks show great promise for making large-scale population genetic inference more automated and flexible.Non UBCUnreviewedAuthor affiliation: Smith CollegeFacult