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Release the 25 Varformer paper models

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README.md ADDED
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+ ---
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+ license: cc-by-nc-sa-4.0
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+ library_name: pytorch
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+ tags:
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+ - biology
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+ - genomics
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+ - drug-discovery
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+ - target-prioritization
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+ - variant-prioritization
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+ ---
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+
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+ # Varformer
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+
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+ Varformer is a multimodal transformer for population-scale drug target and variant prioritization. It learns contextual representations of the missense variants observed in a population and integrates them with gene-level biological features through cross-modal attention. It is trained with a non-negative positive-unlabeled objective for the gene score, and an allele-frequency objective for a per-variant score.
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+
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+ Code, documentation and installation: https://github.com/aaronwtr/Varformer
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+
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+ ## Models in this repository
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+
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+ The 25 models of the paper. Each was trained with five seeds (13, 23, 42, 85, 482), with the variant head (`variant_loss_weight` 0.1, pooled binary cross-entropy) and 32-bit precision.
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+
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+ | Folder | Cohort | Variant source | Gene inputs | Use in the paper |
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+ |---|---|---|---|---|
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+ | `afr/` | African / African American | gnomAD v4.1 | without `geneticConstraint` | gene prioritization |
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+ | `amr/` | Admixed American | gnomAD v4.1 | without `geneticConstraint` | gene prioritization |
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+ | `nfe/` | Non-Finnish European | gnomAD v4.1 | without `geneticConstraint` | gene prioritization |
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+ | `sas/` | South Asian | Genes & Health | without `geneticConstraint` | gene prioritization |
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+ | `sas-gnh/` | South Asian | Genes & Health | with `geneticConstraint` | variant scores in the Genes & Health analyses |
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+
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+ Each model has two files:
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+
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+ - `seed<N>.safetensors`: the model weights only. The files contain no training data, no genotypes and no individual-level information.
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+ - `seed<N>.json`: the training settings, the input dimensions, the class prior, the decision threshold, the selected epoch, the validation Spearman correlation, and SHA-256 checksums of the weights and of the source checkpoint.
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+
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+ `index.json` lists all 25 models.
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+
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+ ## Use
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+
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+ ```python
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+ from varformer import Varformer
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+
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+ model = Varformer.from_pretrained("nfe", seed="best")
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+ predictions = model.predict(genes=["ENSG00000141510"])
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+ variant_scores = model.score_variants(genes=["ENSG00000141510"])
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+ ```
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+
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+ `from_pretrained` checks the weights against their checksum. The model inputs are built from local copies of the input data (gnomAD or Genes & Health variants, AlphaMissense scores, Open Targets, Human Protein Atlas and GTEx features); see the data sources in the code repository. Genes & Health data are available only to approved researchers.
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+
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+ The gene score ranks genes by target-prioritization evidence; it is not a calibrated probability of clinical success. The variant score ranks variants for research prioritization; it is not validated for clinical variant classification.
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+
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+ ## License
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+
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+ The weights are released under [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/): attribution, non-commercial use only, and the same license for derived works. The models take AlphaMissense scores as input, and AlphaMissense is released under the same license. The code is MIT-licensed. For commercial use, train a model with another pathogenicity predictor; the code repository describes how.
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+
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+ ## Citation
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+
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+ The paper citation will be added when the preprint is available. Until then, please cite the software release described in the code repository.
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