No, back in the late 90s and early 00s, people were trying to engineer custom nucleases and transcription factors, my work was on doing molecular dynamics simulations to optimize TF sequence specificity (similar to engineered zinc fingers) for gene therapy. I wrote up my dissertation and published it in 2001, and then went off to find enough compute, IO, and smart people to make it happen (https://research.google/blog/groundbreaking-simulations-by-g...).
replyMy approach would require custom engineering for every different sequence we'd want to target. With CRISPR, you just "program" the system with a guide sequence, you don't need to do massive engineering to solve a protein design problem.
So it's not the situation they described at all, you didn't waste time during the PhD having to scramble to change topics as it happened after you were done.
replyThat, and also it's just a completely different approach which might later on turn out to be useful. People should remember that artificial neural networks were developed decades before they were useful. People were doing all kinds of other approaches to ML like support vector machines before advances in hardware made deep neural nets feasible and therefore interesting again. ANNs were never obsoleted by SVMs.
replyActually, I'm pretty sure SVMs were obsoleted with ANNs (not just in terms of UFFs).
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