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by ak_111
716 days ago
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"...Some cell biologists and biochemists who used to work with structural biologists have replaced them with AlphaFold2 — and take its predictions as truth. Sometimes scientists publish papers featuring protein structures that, to any structural biologist, are obviously incorrect, Perrakis said. “And they say: ‘Well, that’s the AlphaFold structure.’”" It is amazing that this happens. I am not naive about academic standards, but if something is clearly wrong and used in a paper (especially one with consequences on medical health) then it should be quite easy to name-and-shame until the editors of the journal force the authors to make a redaction or correction if the authors don't do it themselves. Otherwise people should start name-and-shame the journal and its reputation should sink. Also I am curious if there are already lists of known incorrect predictions by Alphafold, shouldn't this be published and alphafold's database tag such predictions accordingly to notify users that these particular predictions are proven to be wrong. |
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This is the number one issue with using the so-called "deep learning": the results may be completely wrong and there is no known way to predict when they will be or detect when they are (relying on "deep learning" alone).
The worse issue is that by "deep learning" we learn only the coefficients that give accurate predictions on a training set. Extrapolating the results is the hopeful leap of faith that is known to break down catastrophically on some inputs. The "neural nets" do not give us the new knowledge, but rather, an attractive nuisance of a tool.