The “DeepVariant” example exemplifies the problem with deep learning in biology: some exceptions (when actual imaging is involved) notwithstanding, we still haven’t found a good use for it. Most problems that we can formulate in biology are solvable with linear classifiers: the models are purely additive. The nonlinearity of deep learning offers no benefit.

The article says that

> In tests, DeepVariant performed at least as well as conventional tools.

Which is technically correct but highly misleading. But a more honest way of saying this would be that

> DeepVariant performs no better than conventional tools, and given the nature of the problem there is no reason to expect further boosts in the future from this method.

This is pretty much what the rest of the field thinks, anyway (example of this view in [1]).

From a pure science point of view, DeepVariant is interesting: it applies a new technique to an old problem and shows that it works. This alone is exciting (to me personally at least). But in practical terms it’s useless; it does no better than existing methods and is far more complex and orders of magnitude less efficient.

[1] https://www.forbes.com/sites/stevensalzberg/2017/12/11/no-go...