Deep learning sharpens views of cells and genes

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...

The advances in this field will probably come from people like the biologists quoted at the end of the article, who are using these new techniques to improve workflow and identify new techniques to explore biology

The google stuff, as others have said, seems...useless. Deepvariant performs just as well, but not better, than conventional methods. As far as being able to tell someone's age or smoking status or blood pressure, that is already done pretty well by...asking people, looking at birth certificates or using a blood pressure monitor

A few years ago, google publicized its ability to detect macular degeneration or some disease better than humans using deep learning. But it was only marginally better, not enough to change clinical decisions. And to actually implement that tech would be almost impossible given existing healthcare workflows, treatments and economics. The ability to predict heart attack from eye images is cool in theory, but they probably can't actually do that yet technically with a good enough specificity, and how would you get eye images on a regular enough basis for it to be useful?

even nature is hyping the verily paper now?

It's like I'm taking crazy pills. Variant calling was already very very good and grounded in statistics. Why ON EARTH do we need to convert it to AN IMAGE and give it 1000x more compute? The gains are like 1% too. That's barely above the error rate of sequencing.

Isn't this the software that turns alignments into images to run through an image analysis pipeline? They just jerryrigged alignment data onto an image analysis system?

Does it offer speed/memory advantages over traditional variant calling?

Disappointed by the lack of images.