opentargets/locus_to_gene_merged_efo

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

Click to expand

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 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