Literally every gene expression model ever published has come with some claim of novel biological discovery. If they didn't, they wouldn't be published in the b

Literally every gene expression model ever published has come with some claim of novel biological discovery. If they didn't, they wouldn't be published in the biological literature -- nobody serious cares that a classifier was applied to a dataset. Good papers will go so far as to take a random sample (or even better: all) of such "recommendations" and validate them using experiments. Then you attempt to quantify the success rate, the sensitivity, selectivity, etc. Did they do that here? We go back to the article: > When her team put the prediction to the test in real cells called cardiomyocytes, the beating of the heart cells grew weaker. OK. How many predictions did they test? How many failed for the one that succeeded? ...and later: > Dr. Theodoris’s team followed the model’s advice, knocking down each of the four genes. In two out of the four cases, the treatment improved how the cells contracted. So we have two fairly classic examples of cherry-picked experimental validation. How many predictions did they validate? Did they just select the ones they thought made sense? All we know, from this, is that they selected some subset of results to test, and failed roughly half the time in the latter experiment. That's not a great batting average for this kind of work. None of this is new. We've been applying ML to these problems for decades. Wrapping this kind of stuff up in fuzzy claims of AGI is just another way of escaping scientific rigor via application of hype [1]. [1] To be clear, I'm not accusing the researcher of this. For all I know, the underlying paper is good. But this article is dumb.