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license: apache-2.0 |
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tags: |
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- medical |
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- biology |
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# eccDNAMamba |
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**A Pre-Trained Model for Ultra-Long eccDNA Sequence Analysis** |
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### Model Overview |
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**eccDNAMamba** is a **bidirectional state-space model (SSM)** designed for efficient and topology-aware modeling of **extrachromosomal circular DNA (eccDNA)**. |
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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. |
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The model provides strong performance across cancer-associated eccDNA prediction, copy-number level estimation, and real vs. pseudo-eccDNA discrimination tasks. |
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### Quick Start |
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```python |
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from transformers import AutoTokenizer, AutoModelForMaskedLM |
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tokenizer = AutoTokenizer.from_pretrained("eccdna/eccDNAMamba-1M") |
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model = AutoModelForMaskedLM.from_pretrained("eccdna/eccDNAMamba-1M") |
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sequence = "ATGCGTACGTTAGCGTACGT" |
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inputs = tokenizer(sequence, return_tensors="pt") |
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outputs = model(**inputs) |
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# Access logits or reconstruct masked spans |
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logits = outputs.logits |
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``` |
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--- |
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### Citation |
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```python |
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@inproceedings{ |
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liu2025eccdnamamba, |
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title={ecc{DNAM}amba: A Pre-Trained Model for Ultra-Long ecc{DNA} Sequence Analysis}, |
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author={Zhenke Liu and Jien Li and Ziqi Zhang}, |
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booktitle={ICML 2025 Generative AI and Biology (GenBio) Workshop}, |
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year={2025}, |
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url={https://openreview.net/forum?id=56xKN7KJjy} |
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} |
