chore: update model

README.md

@@ -2,6 +2,7 @@
 # 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)
@@ -16,7 +17,7 @@ The locus-to-gene (L2G) model derives features to prioritise likely causal genes
 
 ## Training Procedure
 
-[More Information Needed]
+Gradient Boosting Classifier
 
 ### Hyperparameters
 
@@ -52,17 +53,14 @@ The locus-to-gene (L2G) model derives features to prioritise likely causal genes
 
 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 at: https://opentargets.github.io/gentropy/python_api/methods/l2g/model/
+    
+    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
 
-# Training Procedure
-
-Gradient Boosting Classifier
-
 # License
 
 MIT

l2g_model_1006.pkl

@@ -1,3 +1,3 @@
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