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
[More Information Needed]
Hyperparameters
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| Hyperparameter | Value |
|---|---|
| ccp_alpha | 0.0 |
| 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 | 1 |
| min_samples_split | 2 |
| min_weight_fraction_leaf | 0.0 |
| n_estimators | 100 |
| n_iter_no_change | |
| random_state | 42 |
| subsample | 1.0 |
| tol | 0.0001 |
| validation_fraction | 0.1 |
| verbose | 0 |
| warm_start | False |
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 at: https://opentargets.github.io/gentropy/python_api/methods/l2g/model/
Citation
https://doi.org/10.1038/s41588-021-00945-5
Training Procedure
Gradient Boosting Classifier
License
MIT