i_love_limes
Joined 7 years ago
Joined 7 years ago
So, it's a bit context dependent, and definitely dependent on other mutations that have occurred in other parts of your DNA. Most complex diseases are "polygenic", meaning it's a culmination of quite a few factors that would contribute to a specific good or bad outcome.
So, yes, it could be modeled as a sort very context dependent with a lot of highly correlated non-independent covariates. We do use quite a lot of statistical and ML methods to understand the genome (I work in statistical genetics), but the complexity of biology has so far proved a tough nut to crack.
This is a common, but poor framing of evolution. Remember: the only real rule with evolution is "did this evolutionary change mean they survived / reproduced more"? Evolution is not a local or global maxima solving function. I find this kind of attempt at interpreting selection common with engineers, programmers, etc.
Hardin did not argue that private industry was more efficient. His paper described that with an unmanaged, private, unregulated open pasture that has no property rights, individuals will exploit it until it collapses. It wasn't used used as justification for privatization, if anything it was the opposite.
Ostrom did not argue that unmanaged resources don't collapse. Instead, she showed with data a third way of organising which was more involved with self-governing, communal rules to manage shared resources without resorting to either a private corporation or government control.
Otherwise it's a really fun idea! Can I suggest you also scrape from https://www.medrxiv.org/? This is where a lot of medical research preprints, not arxiv
So, a specific SNP mutation being predictive of a gene expression / protein is basically a p-value of 0.
Can't speak for physics experiments, but this is almost certainly not a statistical error
That is the minimum threshold. This study found that peak was at p < 1e-37 or so. But that is where the biological analysis begins. Unlike social scientists, we don't stop with the statistical correlation, we then go on to look at what we know about that gene, the type of mutation, if it's a loss or gain of function, what role that gene has in various tissues, etc. And mendelian randomization is another way to unpick the causal direction of effect.
Not to say this is the truth or causal, but it's a lot closer to causal than what you are implying.
https://www.nytimes.com/2024/10/22/us/politics/john-kelly-tr...