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sns-paper-flashzoi-rf64k – AI Model by tanaylab | AlphaNeural AI

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tanaylab/
sns-paper-flashzoi-rf64k

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Flashzoi RF 64k — Trained from Scratch

Borzoi architecture trained from random initialization on mouse ESC-derived CUT&Tag H3K27me3 and H3K4me3 tracks.

Receptive field: 64k
Architecture: Borzoi (from scratch, model name "flashzoi")
Resolution: 32 bp
Training data: Normalized CUT&Tag from mouse ESC-derived embryoid bodies
Paper: Casimov, Lifshitz et al., "Reverse engineering the genomic encodings of the pluripotent epigenome"

Usage

bash

torchrun --nproc_per_node=8 infer_borzoi_pytorch.py \
    --config config.yaml \
    --checkpoint model.safetensors \
    --genome_fasta mm10.fa \
    --output_dir predictions/

See the paper companion repository for full code and configs.