Blame for README.md

---
license: apache-2.0
tags:
- biology
- rna-seq
- tabular-classification
- tabular-regression
- survival-analysis
- in-context-learning
datasets:
- hanzi-bulk-collab/bulk-datasets-private
---

# PRTS

PRTS (Prior-trained RNA-seq Task Solver) predicts binary, multiclass, continuous and survival endpoints from bulk transcriptomes in context. A labelled cohort provides the context for predictions on new samples.

This repository provides the evaluated checkpoint for **In-context learning predicts clinical endpoints from bulk transcriptomes across diseases and platforms**, by Han Wang, Lennart Purucker, Huafeng Liu, Ziyin Luo, Junfeng Hao, Chao Zhu and Chunjie Tian.

- [Code and applications](https://github.com/Hanziwww/PRTS)
- [PreBULK, RealBulk and benchmark data](https://huggingface.co/datasets/hanzi-bulk-collab/bulk-datasets-private)
- Architecture: 219,595,334 parameters in 471 tensors; four cell blocks and twenty row blocks.
- File: `prts.pt`, a PyTorch checkpoint containing `config.model` and the `model` state dictionary.
- Export: model configuration and all parameter tensors are unchanged from the author-supplied checkpoint. Training-run bookkeeping and machine-specific paths are omitted from this inference export.
- [Checkpoint size and SHA-256](checkpoint_metadata.json)

## Load the model

```bash
pip install "git+https://github.com/Hanziwww/PRTS.git#subdirectory=prts" huggingface_hub
```

```python
from huggingface_hub import hf_hub_download
from prts import load

checkpoint = hf_hub_download("HanZiwww/PRTS", "prts.pt")
model = load(checkpoint, device="cpu")  # use "cuda" for a CUDA GPU
```

See the [inference documentation](https://github.com/Hanziwww/PRTS/tree/main/prts) for expression scaling, task construction and prediction outputs. The default Thinking preset averages four passes; the Off preset is also available.

## Scope and limitations

The model was evaluated on retrospective public transcriptomic cohorts. Accuracy varies with endpoint, cohort, platform and the reference samples supplied as context. Cross-cohort and unseen-site evaluation should be considered when assessing transfer to a new study. This repository supplies a research model; it is not a validated clinical diagnostic device.

## Licence

The checkpoint and author-owned code are available under Apache License 2.0. Original datasets and third-party code retain their own terms.