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eccDNAMamba/
eccDNAMamba_real_vs_pseudo_eccdna_arabidopsis_thaliana

Name: eccDNAMamba_real_vs_pseudo_eccdna_arabidopsis_thaliana
Availability: InStock

safetensors

biology

medical

eccDNAMamba/eccDNAMamba-1M

finetune

apache-2.0

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eccDNAMamba

A Pre-Trained Model for Ultra-Long eccDNA Sequence Analysis

Model Overview

eccDNAMamba is a bidirectional state-space model (SSM) designed for efficient and topology-aware modeling of extrachromosomal circular DNA (eccDNA).
By combining forward and reverse Mamba-2 encoders, motif-level Byte Pair Encoding (BPE), and a lightweight head–tail circular augmentation, it captures wrap-around dependencies in ultra-long (10–200 kbp) genomic sequences while maintaining linear-time scalability.
The model provides strong performance across cancer-associated eccDNA prediction, copy-number level estimation, and real vs. pseudo-eccDNA discrimination tasks.

Quick Start

python

from transformers import AutoTokenizer, AutoModelForMaskedLM

tokenizer = AutoTokenizer.from_pretrained("eccdna/eccDNAMamba-1M")
model = AutoModelForMaskedLM.from_pretrained("eccdna/eccDNAMamba-1M")

sequence = "ATGCGTACGTTAGCGTACGT"
inputs = tokenizer(sequence, return_tensors="pt")
outputs = model(**inputs)

# Access logits or reconstruct masked spans
logits = outputs.logits

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