| --- |
| 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. |
|
|
