Session
The Long Tail of A’s, C’s, G’s, T’s: Scaling Deep Learning for Biological Data
Deep learning has rewritten what we can do with biological data. We can predict protein structure, annotate genomes, model regulatory logic, and map mutation impact at astonishing scale. Yet a quieter truth sits behind the breakthroughs:
Most biological AI models never leave the paper.
They live in beautifully written PDFs, get a round of citations, and then sleep in forgotten GitHub repositories. The hard part isn’t modeling. The hard part is deployment.
This article is about that missing middle: how to take deep learning models for DNA, RNA, and proteins from research experiments to real, working production pipelines.
Godhuli Das
Active community host from Kolkata, ERP consultant, and computational sciences researcher working on taking ML and DL models beyond papers and into real production pipelines in computational biology.
Bengaluru, India
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