eccDNAMamba/cancer_eccdna_prediction_short
Cancer-associated eccDNA prediction (short) This dataset contains Cancer-associated eccDNA prediction (short) sampled from healthy and cancer tissues. Each entry contains: sequence: DNA string (A/T/C/G) label: 0 = healthy, 1 = cancer 📁 Folder Structure cancer_eccdna_prediction_short/ ├── data/ │ └── cancer_eccdna_prediction_short.csv ├── hf_data/ │ ├── dataset_info.json │ └── data.arrow └── README.md 🚀 Quick Usage from datasets import… See the full description on the dataset page: https://huggingface.co/datasets/eccDNAMamba/cancer_eccdna_prediction_short.
Cancer-associated eccDNA prediction (short)
This dataset contains Cancer-associated eccDNA prediction (short) sampled from healthy and cancer tissues. Each entry contains:
sequence: DNA string (A/T/C/G)label: 0 = healthy, 1 = cancer
📁 Folder Structure
<pre> cancereccdnapredictionshort/ ├── data/ │ └── cancereccdnapredictionshort.csv ├── hfdata/ │ ├── datasetinfo.json │ └── data.arrow └── README.md </pre>
🚀 Quick Usage
<pre><code class="language-python"> from datasets import loaddataset, loadfrom_disk
Load from local
dataset = loadfromdisk("cancereccdnapredictionshort/hfdata")
Load from Hugging Face Hub (after upload)
dataset = loaddataset("your-username/cancereccdnapredictionshort") </code></pre>
Citation
If you use this dataset, please cite: <pre><code class="language-python">
@inproceedings{liu2025eccdnamamba, title={eccDNAMamba: A Pre-Trained Model for Ultra-Long eccDNA Sequence Analysis}, author={Zhenke Liu and Jien Li and Ziqi Zhang}, booktitle={ICML 2025 GenBio Workshop}, year={2025}, url={https://openreview.net/forum?id=56xKN7KJjy} }