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locus_to_gene_dynamic_distance_noMissing_gs_withPrev

Tabular Classification
Scikit-learn
skops
Model card Files Community
ireneisdoomed commited on
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chore: update model

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Files changed (4) hide show
  1. .gitattributes +1 -0
  2. README.md +169 -0
  3. classifier.skops +3 -0
  4. config.json +155 -0
.gitattributes CHANGED
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  *.zst filter=lfs diff=lfs merge=lfs -text
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README.md ADDED
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+ ---
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+ library_name: sklearn
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+ tags:
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+ - sklearn
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+ - skops
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+ - tabular-classification
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+ model_format: skops
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+ model_file: classifier.skops
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+ widget:
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+ ---
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+
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+ # Model description
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+
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+ 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:
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+
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+ - Distance: (from credible set variants to gene)
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+ - Molecular QTL Colocalization
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+ - Chromatin Interaction: (e.g., promoter-capture Hi-C)
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+ - Variant Pathogenicity: (from VEP)
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+
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+ More information at: https://opentargets.github.io/gentropy/python_api/methods/l2g/_l2g/
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+
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+
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+ ## Intended uses & limitations
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+
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+ [More Information Needed]
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+
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+ ## Training Procedure
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+
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+ Gradient Boosting Classifier
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+
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+ ### Hyperparameters
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+
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+ <details>
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+ <summary> Click to expand </summary>
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+
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+ | Hyperparameter | Value |
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+ |--------------------------|--------------|
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+ | ccp_alpha | 0.0 |
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+ | criterion | friedman_mse |
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+ | init | |
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+ | learning_rate | 0.1 |
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+ | loss | log_loss |
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+ | max_depth | 5 |
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+ | max_features | |
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+ | max_leaf_nodes | |
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+ | min_impurity_decrease | 0.0 |
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+ | min_samples_leaf | 1 |
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+ | min_samples_split | 2 |
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+ | min_weight_fraction_leaf | 0.0 |
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+ | n_estimators | 100 |
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+ | n_iter_no_change | |
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+ | random_state | 42 |
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+ | subsample | 1.0 |
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+ | tol | 0.0001 |
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+ | validation_fraction | 0.1 |
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+ | verbose | 0 |
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+ | warm_start | False |
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+
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+ </details>
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+
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+ # How to Get Started with the Model
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+
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+ 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.
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+ The model can then be used to make predictions using the `predict` method.
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+
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+ More information can be found at: https://opentargets.github.io/gentropy/python_api/methods/l2g/model/
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+
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+
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+ # Citation
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+
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+ https://doi.org/10.1038/s41588-021-00945-5
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+
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+ # License
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+
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+ MIT
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