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Notes -
Apologies for the naive question, but I'm largely ignorant of the nuts and bolts of AI/ML.
Many data formats in biology are just giant arrays, with each row representing a biological cell and columns representing a gene (RNA-Seq), parameter (flow cytometry). Sometimes rows are genetic variants and columns are various characteristics of said variant (minor allele frequency, predicted impact on protein function, etc).
Is there a way to feed this kind of data to LLMs? It seems trivial for chatGPT to parse 'This is an experiment looking at activation of CD8+ T cells, generate me a series of scatterplots and gates showcasing the data' but less trivial to parse the giant 500,000x15 (flow) or 10,000x20,000 (scRNA-Seq) arrays. Or is there a way for LLMs to interact with existing software?
What’s the advantage over normal programming?
This really feels like a pair of 'for' loops instead of a flexible task. You could even go up a level and write a tool that lets you pick different axes.
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