# AIDO.RNA 1.6B

AIDO.RNA is a 1.6B parameter RNA foundation model trained on 42 million non-coding RNA sequences at single-nucleotide resolution. It achieves state-of-the-art performance on a comprehensive set of tasks, including RNA secondary structure prediction, mRNA-related tasks, RNA function prediction tasks, and RNA inverse folding.

<img src="https://cdn-uploads.huggingface.co/production/uploads/63008d4bc1e149ceaff724a3/mNqn5SKQFHxSby3E2dosE.png" alt="description" style="width:80%; height:auto;">

## Model architectural details
TODO

## Pre-training data
TODO

## Downstream evaluation
TODO

## How to Use
Build any downstream models from this backbone

### Get RNA sequence embedding
```
from genbio_finetune.tasks import Embed
model = Embed.from_config({"model.backbone": "rnafm"})
collated_batch = model.collate({"sequences": ["ACGT", "ACGT"]})
embedding = model(collated_batch)
print(embedding.shape)
print(embedding)
```

### Sequence-level classification
```
import torch
from genbio_finetune.tasks import SequenceClassification
model = SequenceClassification.from_config({"model.backbone": "rnafm", "model.n_classes": 2})
collated_batch = model.collate({"sequences": ["ACGT", "ACGT"]})
logits = model(collated_batch)
print(logits)
print(torch.argmax(logits, dim=-1))
```

### Token-level classification
```
import torch
from genbio_finetune.tasks import TokenClassification
model = TokenClassification.from_config({"model.backbone": "rnafm", "model.n_classes": 3})
collated_batch = model.collate({"sequences": ["ACGT", "ACGT"]})
logits = model(collated_batch)
print(logits)
print(torch.argmax(logits, dim=-1))
```


### Pairwise token-level classification
@Sazan TODO


### Sequence-level regression
```
from genbio_finetune.tasks import SequenceRegression
model = SequenceRegression.from_config({"model.backbone": "rnafm"})
collated_batch = model.collate({"sequences": ["ACGT", "ACGT"]})
logits = model(collated_batch)
print(logits)
```

Or use our one-liner CLI to finetune or evaluate any of the above!
```
gbft fit --model SequenceClassification --model.backbone rnafm --data SequenceClassification --data.path <hf_or_local_path_to_your_dataset>
gbft test --model SequenceClassification --model.backbone rnafm --data SequenceClassification --data.path <hf_or_local_path_to_your_dataset>
```

For more information, visit: [Model Generator](https://github.com/genbio-ai/test)

## Citation
Please cite AIDO.RNA using the following BibTeX code:

@inproceedings{ellington2024accurate,
title={Accurate and General {DNA} Representations Emerge from Genome Foundation Models at Scale},
author={Caleb Ellington, Ning Sun, Nicholas Ho, Tianhua Tao, Sazan Mahbub, Yonghao Zhuang, Hongyi Wang, Eric P. Xing, Le Song},
booktitle={NeurIPS 2024 Workshop on AI for New Drug Modalities},
year={2024}
}

## License
@Hongyi TODO