---
library_name: sklearn
tags:
- sklearn
- skops
- tabular-classification
model_format: skops
model_file: classifier.skops
widget:
- structuredData:
    credibleSetConfidence:
    - 0.75
    - 0.75
    - 0.75
    distanceFootprintMean:
    - 0.9722118973731995
    - 0.9389963746070862
    - 0.96164870262146
    distanceFootprintMeanNeighbourhood:
    - 0.9722118973731995
    - 0.9389963746070862
    - 0.96164870262146
    distanceSentinelFootprint:
    - 0.9722118973731995
    - 0.9389963746070862
    - 0.96164870262146
    distanceSentinelFootprintNeighbourhood:
    - 0.9722118973731995
    - 0.9389963746070862
    - 0.96164870262146
    distanceSentinelTss:
    - 0.9722118973731995
    - 0.9389963746070862
    - 0.9519312381744385
    distanceSentinelTssNeighbourhood:
    - 0.9722750186920166
    - 0.9390574097633362
    - 0.9519931077957153
    distanceTssMean:
    - 0.9722118973731995
    - 0.9389963746070862
    - 0.9519312381744385
    distanceTssMeanNeighbourhood:
    - 0.9722750186920166
    - 0.9390574097633362
    - 0.9519931077957153
    eQtlColocClppMaximum:
    - 0.0
    - 0.0
    - 0.0
    eQtlColocClppMaximumNeighbourhood:
    - 0.0
    - 0.0
    - 0.0
    eQtlColocH4Maximum:
    - 0.0
    - 0.0
    - 0.0
    eQtlColocH4MaximumNeighbourhood:
    - 0.0
    - 0.0
    - 0.0
    geneCount500kb:
    - 15.0
    - 15.0
    - 15.0
    geneId:
    - ENSG00000116133
    - ENSG00000162396
    - ENSG00000162398
    goldStandardSet:
    - negative
    - negative
    - negative
    pQtlColocClppMaximum:
    - 0.0
    - 0.0
    - 0.0
    pQtlColocClppMaximumNeighbourhood:
    - 0.0
    - 0.0
    - 0.0
    pQtlColocH4Maximum:
    - 0.0
    - 0.0
    - 0.0
    pQtlColocH4MaximumNeighbourhood:
    - 0.0
    - 0.0
    - 0.0
    proteinGeneCount500kb:
    - 7.0
    - 7.0
    - 7.0
    sQtlColocClppMaximum:
    - 0.0
    - 0.0
    - 0.0
    sQtlColocClppMaximumNeighbourhood:
    - 0.0
    - 0.0
    - 0.0
    sQtlColocH4Maximum:
    - 0.0
    - 0.0
    - 0.0
    sQtlColocH4MaximumNeighbourhood:
    - 0.0
    - 0.0
    - 0.0
    studyLocusId:
    - 02c442ea4fa5ab80586a6d1ff6afa843
    - 02c442ea4fa5ab80586a6d1ff6afa843
    - 02c442ea4fa5ab80586a6d1ff6afa843
    traitFromSourceMappedId:
    - EFO_0004611
    - EFO_0004611
    - EFO_0004611
    vepMaximum:
    - 0.0
    - 0.0
    - 0.0
    vepMaximumNeighbourhood:
    - 0.0
    - 0.0
    - 0.0
    vepMean:
    - 0.0
    - 0.0
    - 0.0
    vepMeanNeighbourhood:
    - 0.0
    - 0.0
    - 0.0
---

# 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

<details>
<summary> Click to expand </summary>

| Hyperparameter           | Value        |
|--------------------------|--------------|
| ccp_alpha                | 0            |
| criterion                | friedman_mse |
| init                     |              |
| learning_rate            | 0.1          |
| loss                     | log_loss     |
| max_depth                | 3            |
| max_features             |              |
| max_leaf_nodes           |              |
| min_impurity_decrease    | 0.0          |
| min_samples_leaf         | 1            |
| min_samples_split        | 5            |
| min_weight_fraction_leaf | 0.0          |
| n_estimators             | 100          |
| n_iter_no_change         |              |
| random_state             | 42           |
| subsample                | 0.7          |
| 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