| --- |
| pretty_name: "PRIMO benchmark inputs" |
| license: other |
| --- |
| |
| # PRIMO benchmark inputs |
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| PRIMO evaluates whether embeddings from biological foundation models preserve clinically relevant information at the patient level. |
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| This repository contains the data used to generate benchmark submissions. Dataset identities are hidden: files are grouped under IDs such as `d001` and `d002`, without disclosing the disease, tissue, or prediction target. Models produce one embedding per sample, and the evaluation is run separately in the [PRIMO Space](https://huggingface.co/spaces/ScientaLab/primo-eval). |
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| ## Contents |
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| `datasets.yaml` lists each dataset's ID, path, number of samples, number of genes, and gene identifier type. |
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| Each dataset directory contains an `expression.h5ad` file with raw counts stored as `AnnData`. Rows are samples identified by `sample_id`. Columns use NCBI gene IDs, with gene symbols included in the metadata. |
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| ## Submit embeddings |
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| 1. Download the data: |
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| ```bash |
| hf download ScientaLab/primo --repo-type dataset --local-dir primo |
| ``` |
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| 2. Generate one embedding per sample for every dataset. Embedding dimensions may differ between datasets. |
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| 3. Combine the embeddings into one CSV, TSV, Parquet, or NPZ file. Tabular files must contain `dataset_id`, `sample_id`, and one column per embedding dimension (`e0`, `e1`, etc.). NPZ files must contain `dataset_ids`, `sample_ids`, and `embeddings`. |
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| 4. Upload the file to the [PRIMO evaluation Space](https://huggingface.co/spaces/ScientaLab/primo-eval). |
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| ## Scoring |
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| For each task, PRIMO trains the same linear probe on fixed cross-validation folds. Classification tasks use AUROC and regression tasks use Pearson correlation. Dataset scores are converted to a 0 to 1 skill score and aggregated by medical specialty. |
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| A submission is ranked only if it covers every dataset. Labels are kept private and evaluation runs server-side. |
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| Each dataset is redistributed under its source's original open license. |
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