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flying_sheep@lemmy.ml 4 days agoI have little experience with graphdb, but a lot of experience with the pain you’re describing. Maintaining schemas is a pain, maybe if you don’t need the performance, you can go that route!
gravitas_deficiency@sh.itjust.works 4 days ago
The thing that interests me about it is that it will be a lot more trivially interrogable by ML stuff (bespoke ML specifically, not LLM), which could glean an absolute shitload of interesting insights for us.
I am an enormous fucking Luddite for a whole swath of reasons when it comes to LLMs, but ML outside of that context can be immensity powerful when employed correctly.
flying_sheep@lemmy.ml 4 days ago
For sure, my lab has been doing that for a long time.
How is graphdb more ML-friendly?
gravitas_deficiency@sh.itjust.works 3 days ago
If you’re doing PSQL (or any typical relational DB flavor), there’s a lot more complexity in terms of understanding the shape of the data, what joins to what, how to optimize queries, etc. Graph DBs are gonna be easier for a model to explore, since they can just do stuff like “I want to see tests with samples that have reactivity to mutation ABC on chromosome 14 over a threshold of X”, which is a lot easier for an ML agent (or less experienced developer, or even a molecular biologist with limited CS/DB experience) to just intuitively evaluate correctly using the syntax of GraphQL than it would be trying to do a shitload of joins between 6 or 7 tables in PSQL.