Old man 1: Life is one trouble after another. I'd be better off dead, better yet, I wish I was never born

Old man 2: True, true, but who has such luck ?.. maybe one in a thousand.

My blog:

I'm also building an open source generic ML library: & .... which I guess might be of interest to some people here

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Oh, ok, the mechanism is familiar to me and in hindsight this makes sense !

But then, my follow-up would be, if all you are doing is up/down-regulating certain proteins or regions encoding several proteins wouldn't you be able to more easily either get the proteins or plasmids or RNAviruses expressing the proteins into the brain ? Which would be temporary but could be long lasting (and cheap) and would not pose this risk

I don't particularly see why the same class of errors in regulatory regions couldn't cause a protein to stop being expressed entirely or accidentally up/down-regulate expression by quite a lot, having similar side effects. But it's getting into the practical details of gene editing implementation so no idea.

Quite confused about the non-coding region edit hypothesis.

Either you mean "non-coding" as in "regulatory" in which case... wouldn't off-target mutation be just as bad?

Or do you mean "non-coding" as in "areas with an undetermined role that we currently assume are likely vestigial" - in which case, wouldn't the therapy have no effect since the regions aren't causal to anything, just correlated? [Or, in the case where I'd have an effect, we ought to assume that those "non-coding" regions are quite causal for many things and thus just as dangerous to edit]

I don't think this would cover the entirety of science, it would just cover the bits that require statistical tests right now. I agree this is not a way to automate science, but statistical models are in themselves not expalinable beyond what a universal modeler is, they are less, since they introduce fake concepts that don't map onto reality, this paradigm doesn't.

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