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0 pointsrandcraw5y ago0 comments

Pharma may be the intended target for the signaling work, but as a data scientist who works in pharma, I can say with certainty that no biologist or chemist here would entertain for a minute any model that can't explain its mechanisms of action. Nor would the FDA, who wants any model not only to accurately predict the intended outcome but also reflect awareness of the contextual circumstances that surround and lead to it.

No competent physician would be satisfied with a disembodied diagnosis. The constituent symptoms and assay metrics that support that diagnosis are essential to know, especially as disease is often complex and dynamic, and no single diagnostic label should ever hope to supplant a deeper understanding of each patient's unique mix of normality and abnormality. A diagnosis using ML may be a useful starting point in treatment, but never should be the endpoint.

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PaulDavisThe1st5y ago

>no biologist or chemist here would entertain for a minute any model that can't explain its mechanisms of action.

Entertain? Who even really knows what means at this point. But I'm fairly convinced that you'd be quite happy to have a theory-free "intuition pump" that could tell you "if you slow down binding with the following 3 membrane proteins, you see roughly double that effect on overall energy use by the cell".

The tool that generates this prediction may be completely unable to give you a "theory" about why this should be so, but then neither will the experiment(s) you do that confirm it to be true.

So, while indeed, ML-style stuff "should never be the endpoint", they can act as a incredibly useful intuition pump/launchpad for ideas and approaches that would otherwise remain inaccessible.

throwawaygh5y ago

That's the mode of use for ML in most industries -- flagging stuff for follow-up by humans. Basically anything that's not real-time works like this.

Most uses of ML in real-time settings look more like hybrid systems -- a little dusting of ML on top of a whole heap of more traditional mathematical modeling/software engineering.

Outside of a few very niche settings, we're still a long way off from "trusting" ML in any meaningful sense.

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