---
library_name: sklearn
tags:
- sklearn
- skops
- tabular-classification
model_format: skops
model_file: classifier.skops
widget:
- structuredData:
    distanceTssMean:
    - 0.01267080195248127
    - 0.36000001430511475
    - 0.27888116240501404
    distanceTssMinimum:
    - 0.00206877407617867
    - 0.36000001430511475
    - 0.27858978509902954
    eqtlColocClppMaximum:
    - 0.0
    - 0.0
    - 0.0
    eqtlColocClppMaximumNeighborhood:
    - 0.0
    - 0.0
    - 0.0
    eqtlColocLlrMaximum:
    - 0.0
    - 0.0
    - 0.0
    eqtlColocLlrMaximumNeighborhood:
    - 0.0
    - 0.0
    - 0.0
    pqtlColocClppMaximum:
    - 0.0
    - 0.0
    - 0.0
    pqtlColocClppMaximumNeighborhood:
    - 0.0
    - 0.0
    - 0.0
    pqtlColocLlrMaximum:
    - 0.0
    - 0.0
    - 0.0
    pqtlColocLlrMaximumNeighborhood:
    - 0.0
    - 0.0
    - 0.0
    sqtlColocClppMaximum:
    - 0.0
    - 0.0
    - 0.0
    sqtlColocClppMaximumNeighborhood:
    - 0.0
    - 0.0
    - 0.0
    sqtlColocLlrMaximum:
    - 0.0
    - 0.0
    - 0.0
    sqtlColocLlrMaximumNeighborhood:
    - 0.0
    - 0.0
    - 0.0
    studyLocusId:
    - -744015116205320819
    - -1490404886320085047
    - 2683384579342349147
    tuqtlColocClppMaximum:
    - 0.0
    - 0.0
    - 0.0
    tuqtlColocClppMaximumNeighborhood:
    - 0.0
    - 0.0
    - 0.0
    tuqtlColocLlrMaximum:
    - 0.0
    - 0.0
    - 0.0
    tuqtlColocLlrMaximumNeighborhood:
    - 0.0
    - 0.0
    - 0.0
    vepMaximum:
    - 0.0
    - 0.0
    - 0.3356514871120453
    vepMaximumNeighborhood:
    - 0.0
    - 0.0
    - 0.3356514871120453
    vepMean:
    - 0.0
    - 0.0
    - 0.1844341903924942
    vepMeanNeighborhood:
    - 0.0
    - 0.0
    - 0.1844341903924942
---

# 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                | 0.0          |
| 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        |

</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