I am not sure what specifically you mean by "AI" here but it's a bit naive (no offence) to think the field is so dumb that it haven't been looking at "AI" for a few years already. See https://www.biorxiv.org/content/10.1101/2023.10.02.560464v1 for instance
nostos/limbus, genoox, engenome, congenica are a few companies/products that I have heard about and have been around for years (the last one was defunct from what I heard last however).
Thanks for the reply. I have read your post but I haven't seen the preprint obviously and without knowing the details I remain skeptical.
> The WGS NICU diagnostic rate is only ~30%, depending on who you ask.
Agreed. It does not automatically mean, however, that it can be significantly improved with better variant interpetation or better analysis of the same wgs data in general sense.
> I'm planning on publishing evals and benchmarks in the next few weeks, but out-of-the-box systems actually don't do very well for a variety of reasons.
Happy to see it. I wish you all the luck and will be the first one praising your solution if I see convincing results.
The post is written almost as if there is no prior art on (germline) variant interpretation. In fact, it is an established niche field with multiple commercial vendors existing for years (and diagnostics for critically ill infants is the most well known use case - google Stephen Kingsmore or Rady's children hospital for one). I'd be surprised if the approach is really something novel at this point.
It is definitely the case that the parents of babies and kids with life-threatening conditions are often one the most motivated people you see on Earth and what they accomplish sometimes is truly incredible. My heart goes out to them including the OP - I can only imagine how hard it must be....
You are asking about one-sided vs two-sided tests. Not really "more often" because formal type 1 error rate is still the same. I'd say two-sided tests leave more space for post-hoc theorizing but there are valid situations when there is no clear one-sided hypothesis a priori. Do we really know whether that the hypothesis should have been "ruder prompts are better"?
I'd say this is benign compared to other ways of (mis)using statistics e.g. looking which way the difference goes and then running one-sided tests or tweaking the setup until one gets "significant" p vals.
EDIT: I looked in the paper again and noticed that they actually did pairwise t-test on all possible combinations of tones. They should have adjusted for multiple testing since they are doing 10 tests (choose 2 from 10) and not one.
I am wondering why would anyone use a t-test when the experiment is clearly modelled by a binomial distribution: 250 independent questions and each one is either answered correctly or not (the null is that the success rate is the same).
Sure? It seems to me that the companies dictate what I consume. Many many times I wanted to buy exactly the same clothes item or shoes to replace an old one (because I know exactly how it'd fit and wear) only to discover it has been discontinued with no obvious "heir". Sometimes only 6 months later...
Whats the percentage of people chasing "fashion", especially after mid 30s?
Looks like it's true that low-carb adapted athletes rely more on fat oxidation during exercise but performance suffers nonetheless because of increased oxygen demands that basically cannot be met.
The real world data transformations can get gnarly very quickly and sql is the perfect common debiminator compared to dplyr which is still niche...
How do you feel about polars?