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--- |
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base_model: |
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- genbio-ai/AIDO.RNA-1.6B |
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license: other |
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--- |
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# AIDO.RNA-1.6B-CDS |
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AIDO.RNA-1.6B-CDS is a domain adaptation model on the coding sequences. It was pre-trained on 9 million coding sequences released by Carlos et al. (2024) [1] based on our [AIDO.RNA-1.6B](https://huggingface.co/genbio-ai/AIDO.RNA-1.6B) model. |
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## How to Use |
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### Build any downstream models from this backbone with ModelGenerator |
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For more information, visit: [Model Generator](https://github.com/genbio-ai/modelgenerator) |
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```bash |
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mgen fit --model SequenceClassification --model.backbone aido_rna_1b600m_cds --data SequenceClassificationDataModule --data.path <hf_or_local_path_to_your_dataset> |
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mgen test --model SequenceClassification --model.backbone aido_rna_1b600m_cds --data SequenceClassificationDataModule --data.path <hf_or_local_path_to_your_dataset> |
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``` |
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### Or use directly in Python |
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#### Embedding |
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```python |
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from modelgenerator.tasks import Embed |
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model = Embed.from_config({"model.backbone": "aido_rna_1b600m_cds"}).eval() |
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transformed_batch = model.transform({"sequences": ["ACGT", "AGCT"]}) |
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embedding = model(transformed_batch) |
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print(embedding.shape) |
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print(embedding) |
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``` |
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#### Sequence-level Classification |
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```python |
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import torch |
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from modelgenerator.tasks import SequenceClassification |
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model = SequenceClassification.from_config({"model.backbone": "aido_rna_1b600m_cds", "model.n_classes": 2}).eval() |
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transformed_batch = model.transform({"sequences": ["ACGT", "AGCT"]}) |
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logits = model(transformed_batch) |
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print(logits) |
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print(torch.argmax(logits, dim=-1)) |
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``` |
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#### Token-level Classification |
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```python |
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import torch |
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from modelgenerator.tasks import TokenClassification |
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model = TokenClassification.from_config({"model.backbone": "aido_rna_1b600m_cds", "model.n_classes": 3}).eval() |
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transformed_batch = model.transform({"sequences": ["ACGT", "AGCT"]}) |
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logits = model(transformed_batch) |
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print(logits) |
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print(torch.argmax(logits, dim=-1)) |
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``` |
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#### Sequence-level Regression |
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```python |
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from modelgenerator.tasks import SequenceRegression |
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model = SequenceRegression.from_config({"model.backbone": "aido_rna_1b600m_cds"}).eval() |
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transformed_batch = model.transform({"sequences": ["ACGT", "AGCT"]}) |
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logits = model(transformed_batch) |
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print(logits) |
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``` |
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### Get RNA sequence embedding |
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```python |
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from genbio_finetune.tasks import Embed |
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model = Embed.from_config({"model.backbone": "aido_rna_1b600m_cds"}).eval() |
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transformed_batch = model.transform({"sequences": ["ACGT", "ACGT"]}) |
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embedding = model(transformed_batch) |
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print(embedding.shape) |
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print(embedding) |
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``` |
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## Citation |
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Please cite AIDO.RNA using the following BibTeX code: |
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``` |
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@inproceedings{zou_large-scale_2024, |
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title = {A Large-Scale Foundation Model for RNA Function and Structure Prediction}, |
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url = {https://www.biorxiv.org/content/10.1101/2024.11.28.625345v1}, |
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doi = {10.1101/2024.11.28.625345}, |
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publisher = {bioRxiv}, |
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author = {Zou, Shuxian and Tao, Tianhua and Mahbub, Sazan and Ellington, Caleb N. and Algayres, Robin and Li, Dian and Zhuang, Yonghao and Wang, Hongyi and Song, Le and Xing, Eric P.}, |
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year = {2024}, |
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booktitle = {NeurIPS 2024 Workshop on AI for New Drug Modalities}, |
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} |
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``` |
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## Reference |
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1. Carlos Outeiral and Charlotte M Deane. Codon language embeddings provide strong signals for use in protein engineering. Nature Machine Intelligence, 6(2):170–179, 2024. |
