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
- 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
Click to expand
| Hyperparameter | Value |
|---|---|
| objective | binary:logistic |
| base_score | |
| booster | |
| callbacks | |
| colsample_bylevel | |
| colsample_bynode | |
| colsample_bytree | 0.8 |
| device | |
| early_stopping_rounds | |
| enable_categorical | False |
| eval_metric | aucpr |
| feature_types | |
| feature_weights | |
| gamma | |
| grow_policy | |
| importance_type | |
| interaction_constraints | |
| learning_rate | |
| max_bin | |
| max_cat_threshold | |
| max_cat_to_onehot | |
| max_delta_step | |
| max_depth | 5 |
| max_leaves | |
| min_child_weight | 10 |
| missing | nan |
| monotone_constraints | |
| multi_strategy | |
| n_estimators | |
| n_jobs | |
| num_parallel_tree | |
| random_state | 777 |
| reg_alpha | 1 |
| reg_lambda | 1.0 |
| sampling_method | |
| scale_pos_weight | 0.8 |
| subsample | 0.8 |
| tree_method | |
| validate_parameters | |
| verbosity | |
| eta | 0.05 |
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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