Beta

Explore
Marketplace
Neural Labs
Chat
Wallet
Docs

Beta

Explore Marketplace Neural Labs Wallet Docs

Build

Manage

My assets Manage wallet Organization

Community

Discord X (Twitter)Telegram

Docs Privacy Policy Terms and Conditions

Proud member of

Proud member of

Docs Cookie Policy

Privacy Policy Terms and Conditions

© 2026 AlphaNeural AI. All rights reserved.

sns-paper-flashzoi-rf4k – AI Model by tanaylab | AlphaNeural AI

You can deploy this model and start earning money today!

tanaylab/
sns-paper-flashzoi-rf4k

like

0

Views

No views yet

Flashzoi RF 4k — Trained from Scratch

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

Receptive field: 4k
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.