chore: update model base model for 26.06.0-dev2 run

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
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+classifier.skops filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+language: en
+library_name: sklearn
+license: mit
+tags:
+- sklearn
+- tabular-classification
+- genomics
+- gwas
+- gene-prioritization
+---
+# Locus-to-Gene (L2G) Model
+
+The locus-to-gene (L2G) model prioritises likely causal genes at each GWAS locus based on genetic and functional genomics features.
+
+## Model Description
+
+This is a **Gradient Boosting Classifier** (XGBoost) trained to predict causal genes at GWAS loci.
+
+Limited to protein-coding genes with available feature data.
+
+**Key Features:**
+- **Distance**: proximity from credible set variants to gene
+- **Molecular QTL Colocalization**: evidence from expression/protein QTL studies
+- **Variant Pathogenicity**: VEP (Variant Effect Predictor) scores
+
+## Usage
+
+```python
+from gentropy.method.l2g.model import LocusToGeneModel
+from gentropy.common.session import Session
+
+# Load model from Hugging Face Hub
+session = Session()
+model = LocusToGeneModel.load_from_hub(
+    session=session,
+    hf_model_id="opentargets/locus_to_gene"
+)
+
+# Make predictions on your L2G feature matrix
+predictions = model.predict(your_feature_matrix, session)
+```
+
+## Training
+
+- **Architecture**: XGBoost Gradient Boosting Classifier
+- **Training Data**: Curated positive/negative gene-locus pairs from Open Targets
+- **Evaluation Metric**: Area under precision-recall curve (AUCPR)
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@article{ghoussaini2021open,
+title={Open Targets Genetics: systematic identification of trait-associated genes using large-scale genetics and functional genomics},
+author={Ghoussaini, Maya and Mountjoy, Edward and Carmona, Maria and others},
+journal={Nature Genetics},
+volume={53},
+pages={1527--1533},
+year={2021},
+doi={10.1038/s41588-021-00945-5}
+}
+```
+
+## More Information
+
+- **Repository**: [opentargets/gentropy](https://github.com/opentargets/gentropy)
+- **Documentation**: [L2G Method Docs](https://opentargets.github.io/gentropy/python_api/methods/l2g/_l2g/)
+- **Developer**: Open Targets

classifier.skops

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+size 229078

config.json

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+{
+  "sklearn": {
+    "columns": [
+      "studyLocusId",
+      "geneId",
+      "goldStandardSet",
+      "eQtlColocClppMaximum",
+      "pQtlColocClppMaximum",
+      "sQtlColocClppMaximum",
+      "eQtlColocH4Maximum",
+      "pQtlColocH4Maximum",
+      "sQtlColocH4Maximum",
+      "eQtlColocClppMaximumNeighbourhood",
+      "pQtlColocClppMaximumNeighbourhood",
+      "sQtlColocClppMaximumNeighbourhood",
+      "eQtlColocH4MaximumNeighbourhood",
+      "pQtlColocH4MaximumNeighbourhood",
+      "sQtlColocH4MaximumNeighbourhood",
+      "distanceSentinelFootprint",
+      "distanceSentinelFootprintNeighbourhood",
+      "distanceFootprintMean",
+      "distanceFootprintMeanNeighbourhood",
+      "distanceTssMean",
+      "distanceTssMeanNeighbourhood",
+      "distanceSentinelTss",
+      "distanceSentinelTssNeighbourhood",
+      "vepMaximum",
+      "vepMaximumNeighbourhood",
+      "vepMean",
+      "vepMeanNeighbourhood",
+      "e2gMean",
+      "e2gMeanNeighbourhood",
+      "geneCount500kb",
+      "proteinGeneCount500kb",
+      "credibleSetConfidence",
+      "transPQtlColocH4Maximum",
+      "transPQtlColocH4MaximumNeighbourhood"
+    ],
+    "sklearn_version": "1.7.2"
+  },
+  "task": "tabular-classification"
+}

requirements.txt

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+scikit-learn==1.7.2
+skops

test.parquet

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train.parquet

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