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--- |
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license: mit |
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base_model: |
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- zhihan1996/DNABERT-2-117M |
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tags: |
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- genomics |
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- bioinformatics |
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- DNA |
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- sequence-classification |
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- introns |
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- exons |
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- DNABERT2 |
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--- |
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# Exons and Introns Classifier |
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DNABERT2 finetuned model for **classifying DNA sequences** into **introns** and **exons**, trained on a large cross-species GenBank dataset (34,627 different species). |
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--- |
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## Architecture |
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- Base model: DNABERT2 |
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- Approach: Full-sequence classification |
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--- |
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## Usage |
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You can use this model through its own custom pipeline: |
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```python |
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from transformers import pipeline |
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pipe = pipeline( |
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task="dnabert2-exon-intron-classification", |
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model="GustavoHCruz/ExInDNABERT2", |
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trust_remote_code=True, |
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) |
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out = pipe( |
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"GCAGCAACAGTGCCCAGGGCTCTGATGAGTCTCTCATCACTTGTAAAG" |
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) |
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print(out) # EXON |
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``` |
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This model uses the same maximum context length as the standard DNABERT2 (512 tokens), but it was trained on DNA sequences of up to 256 nucleotides. |
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The pipeline will automatically truncate the nucleotide sequence they exceed this limit. |
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--- |
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## Custom Usage Information |
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The model expects the same tokens as DNABERT2, ou seja, nucleotídeos de entrada, como por exemplo |
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``` |
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GTAAGGAGGGGGAT |
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``` |
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The model should predict the class label: 0 (Intron) or 1 (Exon). |
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--- |
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## Dataset |
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The model was trained on a processed version of GenBank sequences spanning multiple species, available at the [DNA Coding Regions Dataset](https://huggingface.co/datasets/GustavoHCruz/DNA_coding_regions). |
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--- |
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## Publications |
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- **Full Paper** |
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Achieved **2nd place** at the _Symposium on Knowledge Discovery, Mining and Learning (KDMiLe 2025)_, organized by the Brazilian Computer Society (SBC), held in Fortaleza, Ceará, Brazil. |
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DOI: [https://doi.org/10.5753/kdmile.2025.247575](https://doi.org/10.5753/kdmile.2025.247575). |
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- **Short Paper** |
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Presented at the _IEEE International Conference on Bioinformatics and BioEngineering (BIBE 2025)_, held in Athens, Greece. |
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DOI: [https://doi.org/10.1109/BIBE66822.2025.00113](https://doi.org/10.1109/BIBE66822.2025.00113). |
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--- |
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## Training |
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- Trained on an architecture with 8x H100 GPUs. |
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--- |
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## Metrics |
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**Average accuracy:** **0.9956** |
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| Class | Precision | Recall | F1-Score | |
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| ---------- | --------- | ------ | -------- | |
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| **Intron** | 0.9943 | 0.9922 | 0.9932 | |
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| **Exon** | 0.9962 | 0.9972 | 0.9967 | |
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### Notes |
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- Metrics were computed on a full isolated test set. |
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- The classes follow a ratio of approximately 2 exons to one intron, allowing for direct interpretation of the scores. |
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--- |
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## GitHub Repository |
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The full code for **data processing, model training, and inference** is available on GitHub: |
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[CodingDNATransformers](https://github.com/GustavoHCruz/CodingDNATransformers) |
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You can find scripts for: |
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- Preprocessing GenBank sequences |
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- Fine-tuning models |
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- Evaluating and using the trained models |
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