ireneisdoomed
commited on
Commit
•
2fd7f0b
1
Parent(s):
da9ae65
chore: update model
Browse files- .gitattributes +1 -0
- README.md +169 -0
- classifier.skops +3 -0
- config.json +155 -0
.gitattributes
CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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classifier.skops filter=lfs diff=lfs merge=lfs -text
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README.md
ADDED
@@ -0,0 +1,169 @@
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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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- structuredData:
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distanceTssMean:
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- 0.2199999988079071
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- 0.18174278736114502
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- 0.36000001430511475
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distanceTssMinimum:
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- 0.2199999988079071
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- 0.05885494500398636
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+
- 0.36000001430511475
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+
eqtlColocClppMaximum:
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- 0.0
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+
- 0.4752329885959625
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- 0.0
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+
eqtlColocClppMaximumNeighborhood:
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- 0.0
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- -4.0
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- 0.0
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+
eqtlColocLlrMaximum:
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- 0.0
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- 7.234378337860107
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- 0.0
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+
eqtlColocLlrMaximumNeighborhood:
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- 0.0
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- 0.8298853635787964
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- 0.0
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pqtlColocClppMaximum:
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- 0.0
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- 0.0
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- 0.0
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pqtlColocClppMaximumNeighborhood:
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- 0.0
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- 0.0
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- 0.0
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pqtlColocLlrMaximum:
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- 0.0
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- 0.0
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- 0.0
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pqtlColocLlrMaximumNeighborhood:
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- 0.0
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- 0.0
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- 0.0
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sqtlColocClppMaximum:
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- 0.0
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- 0.0
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- 0.0
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sqtlColocClppMaximumNeighborhood:
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- 0.0
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- 0.0
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- 0.0
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sqtlColocLlrMaximum:
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- 0.0
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- 0.0
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- 0.0
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sqtlColocLlrMaximumNeighborhood:
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- 0.0
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- 0.0
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- 0.0
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studyLocusId:
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- -5432966661404469451
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- 3577612979987322946
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- -1490404886320085047
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tuqtlColocClppMaximum:
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- 0.0
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- 0.4703633785247803
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- 0.0
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tuqtlColocClppMaximumNeighborhood:
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- 0.0
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- -2.303677558898926
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- 0.0
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tuqtlColocLlrMaximum:
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- 0.0
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- 7.182735443115234
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- 0.0
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tuqtlColocLlrMaximumNeighborhood:
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- 0.0
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- 0.8265543580055237
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- 0.0
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vepMaximum:
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- 0.0
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- 0.04004563018679619
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- 0.0
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vepMaximumNeighborhood:
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- 0.0
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- 0.04004563018679619
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- 0.0
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vepMean:
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- 0.0
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- 0.026208732277154922
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- 0.0
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vepMeanNeighborhood:
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- 0.0
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- 0.026208732277154922
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- 0.0
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---
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# Model description
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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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- 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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More information at: https://opentargets.github.io/gentropy/python_api/methods/l2g/_l2g/
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## Intended uses & limitations
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[More Information Needed]
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## Training Procedure
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Gradient Boosting Classifier
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### Hyperparameters
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<details>
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<summary> Click to expand </summary>
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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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</details>
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# How to Get Started with the Model
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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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More information can be found at: https://opentargets.github.io/gentropy/python_api/methods/l2g/model/
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# Citation
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https://doi.org/10.1038/s41588-021-00945-5
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# License
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MIT
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classifier.skops
ADDED
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version https://git-lfs.github.com/spec/v1
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oid sha256:3e5d3a8334a1ecb5d49bd6f46ccc38f84adf931a10a6363491b544a2206c5a78
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size 2831537
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config.json
ADDED
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{
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"sklearn": {
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"columns": [
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"studyLocusId",
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"distanceTssMean",
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"distanceTssMinimum",
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"vepMaximumNeighborhood",
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"vepMaximum",
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"vepMeanNeighborhood",
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"vepMean",
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"eqtlColocClppMaximum",
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"eqtlColocClppMaximumNeighborhood",
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"pqtlColocClppMaximum",
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"pqtlColocClppMaximumNeighborhood",
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"sqtlColocClppMaximum",
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"sqtlColocClppMaximumNeighborhood",
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"tuqtlColocClppMaximum",
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"tuqtlColocClppMaximumNeighborhood",
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"eqtlColocLlrMaximum",
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"eqtlColocLlrMaximumNeighborhood",
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"pqtlColocLlrMaximum",
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"pqtlColocLlrMaximumNeighborhood",
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"sqtlColocLlrMaximum",
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"sqtlColocLlrMaximumNeighborhood",
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"tuqtlColocLlrMaximum",
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"tuqtlColocLlrMaximumNeighborhood"
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],
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"environment": [
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"scikit-learn=1.5.1"
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],
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"example_input": {
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"distanceTssMean": [
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33 |
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],
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"distanceTssMinimum": [
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],
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"eqtlColocClppMaximum": [
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"eqtlColocClppMaximumNeighborhood": [
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"eqtlColocLlrMaximum": [
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],
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"studyLocusId": [
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],
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"tuqtlColocClppMaximum": [
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"vepMaximum": [
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"vepMean": [
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]
|
147 |
+
},
|
148 |
+
"model": {
|
149 |
+
"file": "classifier.skops"
|
150 |
+
},
|
151 |
+
"model_format": "skops",
|
152 |
+
"task": "tabular-classification",
|
153 |
+
"use_intelex": false
|
154 |
+
}
|
155 |
+
}
|