README.md

metadata

library_name: sklearn
tags:
  - sklearn
  - skops
  - tabular-classification
model_format: skops
model_file: classifier.skops
widget:
  - structuredData:
      credibleSetConfidence:
        - 0.75
        - 0.75
        - 0.75
      dhsPmtrCorrelationMean:
        - 0
        - 0
        - 0
      dhsPmtrCorrelationMeanNeighbourhood:
        - 0
        - 0
        - 0
      distanceFootprintMean:
        - 0.8487144112586975
        - 0.9365111589431763
        - 0.9975032806396484
      distanceFootprintMeanNeighbourhood:
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        - 0.9365111589431763
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      distanceSentinelFootprint:
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      distanceSentinelFootprintNeighbourhood:
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      distanceSentinelTss:
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        - 0.9257694482803345
        - 0.9975032806396484
      distanceSentinelTssNeighbourhood:
        - 0.850220799446106
        - 0.9274126291275024
        - 0.9992737770080566
      distanceTssMean:
        - 0.8487144112586975
        - 0.9257694482803345
        - 0.9975032806396484
      distanceTssMeanNeighbourhood:
        - 0.850220799446106
        - 0.9274126291275024
        - 0.9992737770080566
      eQtlColocClppMaximum:
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        - 0
        - 0
      eQtlColocClppMaximumNeighbourhood:
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        - 0
        - 0
      eQtlColocH4Maximum:
        - 0
        - 0
        - 0
      eQtlColocH4MaximumNeighbourhood:
        - 0
        - 0
        - 0
      enhTssCorrelationMean:
        - 0
        - 0
        - 0
      enhTssCorrelationMeanNeighbourhood:
        - 0
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      geneCount500kb:
        - 20
        - 20
        - 20
      pQtlColocClppMaximum:
        - 0
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      pQtlColocClppMaximumNeighbourhood:
        - 0
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      pQtlColocH4Maximum:
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        - 0
      pQtlColocH4MaximumNeighbourhood:
        - 0
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        - 0
      pchicMean:
        - 0
        - 0
        - 0
      pchicMeanNeighbourhood:
        - 0
        - 0
        - 0
      proteinGeneCount500kb:
        - 8
        - 8
        - 8
      sQtlColocClppMaximum:
        - 0
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        - 0
      sQtlColocClppMaximumNeighbourhood:
        - 0
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      sQtlColocH4Maximum:
        - 0
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        - 0
      sQtlColocH4MaximumNeighbourhood:
        - 0
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      studyLocusId:
        - 005bc8624f8dd7f7c7bc63e651e9e59d
        - 005bc8624f8dd7f7c7bc63e651e9e59d
        - 005bc8624f8dd7f7c7bc63e651e9e59d
      traitFromSourceMappedId:
        - EFO_0004612
        - EFO_0004612
        - EFO_0004612
      vepMaximum:
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      vepMaximumNeighbourhood:
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      vepMean:
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      vepMeanNeighbourhood:
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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
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	5
min_samples_split	5
min_weight_fraction_leaf	0.0
n_estimators	100
n_iter_no_change
random_state	42
subsample	1
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