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| license: apache-2.0 |
| tags: |
| - medical |
| - biology |
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| # eccDNAMamba |
| **A Pre-Trained Model for Ultra-Long eccDNA Sequence Analysis** |
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| ### 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. |
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| ### Quick Start |
| ```python |
| from transformers import AutoTokenizer, AutoModelForMaskedLM |
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| tokenizer = AutoTokenizer.from_pretrained("eccdna/eccDNAMamba-1M") |
| model = AutoModelForMaskedLM.from_pretrained("eccdna/eccDNAMamba-1M") |
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| sequence = "ATGCGTACGTTAGCGTACGT" |
| inputs = tokenizer(sequence, return_tensors="pt") |
| outputs = model(**inputs) |
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| # Access logits or reconstruct masked spans |
| logits = outputs.logits |
| ``` |
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| ### Citation |
| ```python |
| @inproceedings{ |
| liu2025eccdnamamba, |
| title={ecc{DNAM}amba: A Pre-Trained Model for Ultra-Long ecc{DNA} Sequence Analysis}, |
| author={Zhenke Liu and Jien Li and Ziqi Zhang}, |
| booktitle={ICML 2025 Generative AI and Biology (GenBio) Workshop}, |
| year={2025}, |
| url={https://openreview.net/forum?id=56xKN7KJjy} |
| } |
