README.md

---
language:
- en
tags:
- biology
- dna
- genomics
- metagenomics
- classifier
- awd-lstm
- transfer-learning
license: mit
pipeline_tag: text-classification
library_name: pytorch
---

# LookingGlass Optimal Temperature Classifier

Identifies whether a DNA read originates from an enzyme with a psychrophilic (<15°C), mesophilic (20-40°C), or thermophilic (>50°C) optimal temperature with 70.1% accuracy.

This is a **pure PyTorch implementation** fine-tuned from the LookingGlass base model.

## Links

- **Paper**: [Deep learning of a bacterial and archaeal universal language of life enables transfer learning and illuminates microbial dark matter](https://doi.org/10.1038/s41467-022-30070-8) (Nature Communications, 2022)
- **GitHub**: [ahoarfrost/LookingGlass](https://github.com/ahoarfrost/LookingGlass)
- **Base Model**: [HoarfrostLab/lookingglass-v1](https://huggingface.co/HoarfrostLab/lookingglass-v1)

## Citation

```bibtex
@article{hoarfrost2022deep,
  title={Deep learning of a bacterial and archaeal universal language of life
         enables transfer learning and illuminates microbial dark matter},
  author={Hoarfrost, Adrienne and Aptekmann, Ariel and Farfanuk, Gaetan and Bromberg, Yana},
  journal={Nature Communications},
  volume={13},
  number={1},
  pages={2606},
  year={2022},
  publisher={Nature Publishing Group}
}
```

## Model

| | |
|---|---|
| Architecture | LookingGlass encoder + classification head |
| Encoder | AWD-LSTM (3-layer, unidirectional) |
| Classes | 3 classes: psychrophilic, mesophilic, thermophilic |
| Parameters | ~17M |

## Installation

```bash
pip install torch
git clone https://huggingface.co/HoarfrostLab/LGv1_OptimalTempClassifier
cd LGv1_OptimalTempClassifier
```

## Usage

```python
from lookingglass_classifier import LookingGlassClassifier, LookingGlassTokenizer

model = LookingGlassClassifier.from_pretrained('.')
tokenizer = LookingGlassTokenizer()
model.eval()

inputs = tokenizer(["GATTACA", "ATCGATCGATCG"], return_tensors=True)

# Get predictions
predictions = model.predict(inputs['input_ids'])
print(predictions)  # tensor([class_idx, class_idx])

# Get probabilities
probs = model.predict_proba(inputs['input_ids'])
print(probs.shape)  # torch.Size([2, 3])

# Get raw logits
logits = model(inputs['input_ids'])
print(logits.shape)  # torch.Size([2, 3])
```

## License

MIT License