Comment by mfld
3 hours ago
> For now, this is mostly a story about how AI can be used to parse existing data to discover new biology (which is fantastic!).
I'd like to expand that: in my view, this is also a story of how agentic AI systems can come up with bioinformatics strategies to discover novel features. One would think such a task would be the ideal domain of the genome language models, which have learned the structure and functional relationships of DNA/RNA sequences. The agents instead relied on classical bioinformatics methods such as HMMs to make their discovery.
Note: I could not find the Supplementary Note 1 that was supposed to describe how exactly agents came to their solution, but I assume it was autonomous.
It’s interesting that models seem, to a distant outsider of biology and drug discovery like me, to be good at coming to new conclusions from existing data. I feel like in coding, it’s the opposite - I have to drag the models kicking and screaming towards anything resembling a novel or nuanced approach to some problems. If anything, this behavior in coding is why we say senior+ engineers will continue to be high value employees, because we can steer the models away from boilerplate and overly generic solutions towards ones that fit aspects of the domain we understand more intrinsically.
This could easily just be how it looks from the outside of biology, but it does seem to produce more novel conclusions in biology than it does in coding and art. Curious if others have counter examples…