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I 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!
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.
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.
I 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!
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.
For sure, my lab has been doing that for a long time.
How is graphdb more ML-friendly?
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.