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---
library_name: sklearn
tags:
- sklearn
- skops
- tabular-classification
model_format: skops
model_file: classifier.skops
widget:
- structuredData:
credibleSetConfidence:
- 0.75
- 0.75
- 0.75
dhsPmtrCorrelationMean:
- 0.0
- 0.0
- 0.0
dhsPmtrCorrelationMeanNeighbourhood:
- 0.0
- 0.0
- 0.0
distanceFootprintMean:
- 0.8487144112586975
- 0.9365111589431763
- 0.9975032806396484
distanceFootprintMeanNeighbourhood:
- 0.8487144112586975
- 0.9365111589431763
- 0.9975032806396484
distanceSentinelFootprint:
- 0.8487144112586975
- 0.9365111589431763
- 0.9975032806396484
distanceSentinelFootprintNeighbourhood:
- 0.8487144112586975
- 0.9365111589431763
- 0.9975032806396484
distanceSentinelTss:
- 0.8487144112586975
- 0.9257694482803345
- 0.9975032806396484
distanceSentinelTssNeighbourhood:
- 0.850220799446106
- 0.9274126291275024
- 0.9992737770080566
distanceTssMean:
- 0.8487144112586975
- 0.9257694482803345
- 0.9975032806396484
distanceTssMeanNeighbourhood:
- 0.850220799446106
- 0.9274126291275024
- 0.9992737770080566
eQtlColocClppMaximum:
- 0.0
- 0.0
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eQtlColocClppMaximumNeighbourhood:
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eQtlColocH4Maximum:
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eQtlColocH4MaximumNeighbourhood:
- 0.0
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- 0.0
enhTssCorrelationMean:
- 0.0
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- 0.0
enhTssCorrelationMeanNeighbourhood:
- 0.0
- 0.0
- 0.0
geneCount500kb:
- 20.0
- 20.0
- 20.0
pQtlColocClppMaximum:
- 0.0
- 0.0
- 0.0
pQtlColocClppMaximumNeighbourhood:
- 0.0
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pQtlColocH4Maximum:
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- 0.0
pQtlColocH4MaximumNeighbourhood:
- 0.0
- 0.0
- 0.0
pchicMean:
- 0.0
- 0.0
- 0.0
pchicMeanNeighbourhood:
- 0.0
- 0.0
- 0.0
proteinGeneCount500kb:
- 8.0
- 8.0
- 8.0
sQtlColocClppMaximum:
- 0.0
- 0.0
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sQtlColocClppMaximumNeighbourhood:
- 0.0
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sQtlColocH4Maximum:
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sQtlColocH4MaximumNeighbourhood:
- 0.0
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- 0.0
studyLocusId:
- 005bc8624f8dd7f7c7bc63e651e9e59d
- 005bc8624f8dd7f7c7bc63e651e9e59d
- 005bc8624f8dd7f7c7bc63e651e9e59d
traitFromSourceMappedId:
- EFO_0004612
- EFO_0004612
- EFO_0004612
vepMaximum:
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vepMaximumNeighbourhood:
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vepMean:
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vepMeanNeighbourhood:
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---
# Model description
The locus-to-gene (L2G) model derives features to prioritise likely causal genes at each GWAS locus based on genetic and functional genomics features. The main categories of predictive features are:
- Distance: (from credible set variants to gene)
- Molecular QTL Colocalization
- Chromatin Interaction: (e.g., promoter-capture Hi-C)
- Variant Pathogenicity: (from VEP)
More information at: https://opentargets.github.io/gentropy/python_api/methods/l2g/_l2g/
## Intended uses & limitations
[More Information Needed]
## Training Procedure
Gradient Boosting Classifier
### Hyperparameters
<details>
<summary> Click to expand </summary>
| Hyperparameter | Value |
|--------------------------|--------------|
| ccp_alpha | 0 |
| criterion | friedman_mse |
| init | |
| learning_rate | 0.1 |
| loss | log_loss |
| max_depth | 5 |
| max_features | |
| max_leaf_nodes | |
| min_impurity_decrease | 0.0 |
| min_samples_leaf | 5 |
| min_samples_split | 5 |
| min_weight_fraction_leaf | 0.0 |
| n_estimators | 100 |
| n_iter_no_change | |
| random_state | 42 |
| subsample | 1 |
| tol | 0.0001 |
| validation_fraction | 0.1 |
| verbose | 0 |
| warm_start | False |
</details>
# How to Get Started with the Model
To use the model, you can load it using the `LocusToGeneModel.load_from_hub` method. This will return a `LocusToGeneModel` object that can be used to make predictions on a feature matrix.
The model can then be used to make predictions using the `predict` method.
More information can be found at: https://opentargets.github.io/gentropy/python_api/methods/l2g/model/
# Citation
https://doi.org/10.1038/s41588-021-00945-5
# License
MIT
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