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Proposal: Out-of-core Random Forest Classifier for Ag1000G using Dask #1342

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@Schronding

Hi MalariaGen team (and @jonbrenas),

I have been auditing the gsoc-mosquito-taxon-classifier repository and the current state of the taxonomic classification pipeline. I see the foundation for feature extraction is laid out, but the predictive engine and the orchestration for large-scale processing are pending.

I am currently architecting a scalable machine learning pipeline as part of an advanced university research project, focusing specifically on the Ag1000G dataset. My objective is to implement a Random Forest classifier optimized for out-of-core computation using Dask and Zarr. This architecture will allow parallel processing of massive genomic arrays efficiently, minimizing RAM bottlenecks in environments like Google Colab.

I will be developing and iterating on this infrastructure over the coming months. Since my goal is to eventually contribute this predictive engine back to malariagen-data-python, I wanted to open this thread to ensure this technical approach aligns with your current roadmap for the taxonomy classifier.

Looking forward to your thoughts.

Best regards, Brayan

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