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The problem with this machine-learned “predictive biology” framework is that it doesn’t have any prescription for what to do when your predictions fail. Just co
by bglazer 1y ago
The problem with this machine-learned “predictive biology” framework is that it doesn’t have any prescription for what to do when your predictions fail. Just collect more data! What kind of data? As the author notes, the configuration space of biology is effectively infinite so it matters a great deal what you measure and how you measure it. If you don’t think about this (or your model can’t help you think about it) you’re unlikely to observe the conditions where your predictions are incorrect. That’s why other modeling approaches care about tedious things like physics and causality. They let you constrain the model to conditions you’ve observed and hypothesize what missing, unobserved factors might be influencing your system.
It’s also a bit arrogant in presuming that no other approaches to modeling cells cared about “prediction”. Of course, systems and mathematical biologists care about making accurate predictions, they just also care about other things like understanding molecular interactions *because that lets you make better predictions*
Not to be cynical but this seems like an attempt to export benchmark culture from ML into bio. I think that blindly maximizing test set accuracy is likely to lead down a lot dead end paths. I say this as someone actively doing ML for bio research.
- j7ake 1y agoAlso predictions in biology take months or years to validate, so they lack the fast feedback loop of the vision and NLP world where the feedback is almost instant. Combine this with the fact that In vivo data in biology is extremely limited, and we see copying the NLP and vision playbook into biology is challenging
- Fomite 1y agoThis. Many of the predictions we're talking about are potentially years in the making, involve expensive data collection to validate, suffer from a lot of stochastic noise, etc.
- j7ake 1y agoHonestly even if a prediction comes an experiment, and they know exactly how the experiment was done, it takes month to years to follow up and verify. Generative AI is basically going to flood the field with more predictions, but with little explanation of how, and doing nothing to alleviate the downstream verification process.
- Fomite 1y agoAnd when it's off in its prediction, without an explanation of how, you have no chance to revise your prediction, it's just all the way back to square one.