Dan Schrider and Andy Kern have a new review preprint out, Machine Learning for Population Genetics: A New Paradigm. On Twitter there has already been a little snark to the effect of “oh, you mean regression?” That’s fair enough, and the preprint would probably benefit from a lower key title, though that’s really the sort of titles journals seem to love.
I would recommend this preprint to two large groups of my readers. There are those with strong computational skills who are curious about biology. It makes it clear why population genomics benefits from machine learning methods. Second, those who are interested or trained in genetics with less of a computational and pop gen background.
Yes, all models are wrong. But some give insight, and some are just not salvageable. In population genomics some of the model-building is obviously starting to yield really fragile results.
Seems strange that they left out Latent Dirichlet Allocation (LDA), which spawned a whole field of machine learning methods. Something very close to the original LDA approach was proposed by Pritchard, Stephens, and Donnelly in the original Structure paper (Genetics 2000), 3 years before similar approaches were proposed in the machine learning literature.