Unannotated genomic data favors unsupervised learning
Eric Nguyen says most genomic data is not annotated, making it desirable to learn from it through unsupervised methods.
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原始摘录
there's a lot more data, a lot more genomic data that's not annotated. Actually, most of it, pretty much in many ways, almost all of it is not annotated. And so being able to learn from an unsupervised manner, hugely desirable
Ensemble comparisons require more than one favorable result
Eric Nguyen describes comparing his team’s work with ensemble methods that combine several approaches. He says the team did not want to select one model and simply claim to be better than it.
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原始摘录
they'll take the best methods and kind of do Do an ensemble, right? So they'll take up another, even if the best method is another previous model, they'll mix it with like an SVM and just like throw the kitchen sink at it. And so you can see why it would be the best, right? And so that was the bar for us. We're like, if they're going to throw the kitchen sink at it, like we're not going to cherry pick one model and say we're better than that.
Promising computational results still need wet-lab validation
Eric Nguyen says wet-lab validation was in progress and had not been shown in the discussion. He regards the approach as potentially valuable if it works in the lab.
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原始摘录
We were actually in the process of validating the wet lab right now. So we didn't get to show it here, but we wanted to know, right? Actually, can it not just do this in silico, which it can, it showcased that it was able to continue And now we think this is a, you know, obviously, if this works in the lab, we think this is a hugely, hugely valuable paradigm
nafyi.