Not if (a) it misses a line of research has been refuted 1-2 years ago, (b) the experiments at recommends (RNA-Seq) are a limited resource that requires a whole lab to be setup to efficiently act based upon it, and (c) the result of the work is genetic upregulation of a gene, which could mean just about anything.

Genetic regulation can at best let us know _involvement_ of a gene, but nothing about why. Some examples of why a gene might be involved: it's a compensation mechanism (good!), it modulates the timing of the actual critical processes (discovery worthy but treatment path neutral), it is causative of a disease (treatment potential found) etc...

We don't need pipelines for faster scientific thinking ... especially if the result is experts will have to re-validate each finding. Most experts are anyway truly limited by access to models or access to materials. I certainly don't have a shortage of "good" ideas, and no machine will convince me they're wrong without doing the actual experiments. ;)

This is, I think, what I've been struggling to get across to people: while some domains have problems that you can test entirely in code, there are a lot more where the bottleneck is too resource-conatrained in the physical world to have an experiment-free researcher have any value.

There's practically negative utility for detecting archeological sites in South America, for example: we already know about far more than we could hope to excavate. The ideas aren't the bottleneck.

There's always been an element of this in AI: RL is amazing if you have some way to get ground truth for your problem, and a giant headache if you don't. And so on. But I seem to have trouble convincing people that sometimes the digital is insufficient.