And various dedicated AI models all made poor predictions of the results of that experiment, which casts doubts about the value of the AlphaGenome predictive map.

A virus is much simpler than a human, but even for that simple virus the effects of most of the mutations could not be predicted. A half of the mutations had harmful effects, and for a half of those it is unknown for now why they were harmful.

For a human the uncertainty about the effects of a mutation will be far greater than for one of the simplest viruses.

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nomel 20 hours ago | parent [-]

> A virus is much simpler than a human, but even for that simple virus the effects of most of the mutations could not be predicted.

Is this the correct logic? I (very naively) would think that a small mutation of a virus, with the percent change that is being made, would be more of a fundamental change in the nature of the thing, so maybe less predictable. Where poking genes of a human being more of a (possibly strong!) statistical nudge, since most things seem to be spread across many genes, what's being expressed for sometimes environmental reasons, and even influenced by "junk DNA".

	▲	stopping 20 hours ago \| parent [-]
		I think you have it completely backwards. A smaller genome makes small tweaks much more likely to produce measurable effects, which means your data quality is much better. A larger genome with more gene interaction is more likely to produce non-measurable or non-subtle effects with perturbations of a single parameter. If a positive effect can only be measured by simultaneously mutating multiple inter-dependent genes, and you don't have a data point with those mutations, the effect can never be predicted. The model may even predict the exact opposite: if you have a bunch of data points showing that varying one gene at a time causes the organism to die, the model will predict that the sum of those mutations also causes death. With larger genomes you need exponentially more data points to discover subtle effects.