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--- |
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library_name: sklearn |
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tags: |
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- sklearn |
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- skops |
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- tabular-classification |
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model_format: skops |
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model_file: classifier.skops |
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widget: |
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- structuredData: |
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distanceTssMean: |
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- 0.005956897512078285 |
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- 0.0535997599363327 |
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- 0.0007216916419565678 |
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distanceTssMinimum: |
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- 0.00023104190768208355 |
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- 0.008684908039867878 |
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- 0.0 |
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eqtlColocClppMaximum: |
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- 0.0 |
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- 0.0 |
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- 2.9394341254374012e-05 |
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eqtlColocClppMaximumNeighborhood: |
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- -1.0844675302505493 |
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- 0.0 |
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- -2.4551262855529785 |
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eqtlColocLlrMaximum: |
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- 0.0 |
|
- 0.0 |
|
- -5.864833831787109 |
|
eqtlColocLlrMaximumNeighborhood: |
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- 0.6375470161437988 |
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- 0.0 |
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- -0.6227747797966003 |
|
pqtlColocClppMaximum: |
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- 0.0 |
|
- 0.0 |
|
- 0.0 |
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pqtlColocClppMaximumNeighborhood: |
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- 0.0 |
|
- 0.0 |
|
- 0.0 |
|
pqtlColocLlrMaximum: |
|
- 0.0 |
|
- 0.0 |
|
- 0.0 |
|
pqtlColocLlrMaximumNeighborhood: |
|
- 0.0 |
|
- 0.0 |
|
- 0.0 |
|
sqtlColocClppMaximum: |
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- 0.0 |
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- 0.0 |
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- 0.0 |
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sqtlColocClppMaximumNeighborhood: |
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- -1.75723135471344 |
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- 0.0 |
|
- -3.7946090698242188 |
|
sqtlColocLlrMaximum: |
|
- 0.0 |
|
- 0.0 |
|
- 0.0 |
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sqtlColocLlrMaximumNeighborhood: |
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- 0.5101715922355652 |
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- 0.0 |
|
- 0.5695658922195435 |
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studyLocusId: |
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- -3543201973216145411 |
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- -4859077617144690060 |
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- -870008257560905822 |
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tuqtlColocClppMaximum: |
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- 0.014770692214369774 |
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- 0.0 |
|
- 0.0 |
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tuqtlColocClppMaximumNeighborhood: |
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- -2.5447564125061035 |
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- 0.0 |
|
- -2.497274160385132 |
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tuqtlColocLlrMaximum: |
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- 2.057318925857544 |
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- 0.0 |
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- 0.0 |
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tuqtlColocLlrMaximumNeighborhood: |
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- 0.35586467385292053 |
|
- 0.0 |
|
- -0.7435243129730225 |
|
vepMaximum: |
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- 0.003306703409180045 |
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- 0.0 |
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- 5.660330498358235e-05 |
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vepMaximumNeighborhood: |
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- 0.005385574419051409 |
|
- 0.0 |
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- 0.026831166818737984 |
|
vepMean: |
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- 0.001106836018152535 |
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- 0.0 |
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- 1.4581254617951345e-05 |
|
vepMeanNeighborhood: |
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- 0.0007926996913738549 |
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- 0.0 |
|
- 0.00018241332145407796 |
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--- |
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# Model description |
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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: |
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- Distance: (from credible set variants to gene) |
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- Molecular QTL Colocalization |
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- Chromatin Interaction: (e.g., promoter-capture Hi-C) |
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- Variant Pathogenicity: (from VEP) |
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More information at: https://opentargets.github.io/gentropy/python_api/methods/l2g/_l2g/ |
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## Intended uses & limitations |
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[More Information Needed] |
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## Training Procedure |
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Gradient Boosting Classifier |
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### Hyperparameters |
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<details> |
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<summary> Click to expand </summary> |
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| Hyperparameter | Value | |
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|--------------------------|--------------| |
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| ccp_alpha | 0.0 | |
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| criterion | friedman_mse | |
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| init | | |
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| learning_rate | 0.1 | |
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| loss | log_loss | |
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| max_depth | 5 | |
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| max_features | | |
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| max_leaf_nodes | | |
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| min_impurity_decrease | 0.0 | |
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| min_samples_leaf | 1 | |
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| min_samples_split | 2 | |
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| min_weight_fraction_leaf | 0.0 | |
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| n_estimators | 100 | |
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| n_iter_no_change | | |
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| random_state | 42 | |
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| subsample | 1.0 | |
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| tol | 0.0001 | |
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| validation_fraction | 0.1 | |
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| verbose | 0 | |
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| warm_start | False | |
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</details> |
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# How to Get Started with the Model |
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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. |
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The model can then be used to make predictions using the `predict` method. |
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More information can be found at: https://opentargets.github.io/gentropy/python_api/methods/l2g/model/ |
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# Citation |
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https://doi.org/10.1038/s41588-021-00945-5 |
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# License |
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MIT |
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