Update README.md

README.md

@@ -27,3 +27,46 @@ configs:
   - split: test
     path: data/test-*
 ---
+
+[DLKcat](https://github.com/SysBioChalmers/DLKcat) (BRENDA and SABIO-RK) with splits from [Biomap](https://huggingface.co/datasets/Bo1015/enzyme_catalytic_efficiency), and repeated and short sequences removed. Enzymes with multiple reactions have their kcat averaged.
+
+The kcat is log10 normalized, so the unit is log10(1/s). However, because it is averaged over reactions and also reaction ambiguous, it is really just a general proxy for catalytic rate. Higher is faster.
+
+Processing:
+```
+import pandas as pd
+from datasets import Dataset, DatasetDict, concatenate_datasets
+
+def process_dataset(dataset_dict):
+    precedence = ['train', 'valid', 'test']
+    # Add a 'split' column to each dataset
+    for split in dataset_dict.keys():
+        dataset_dict[split] = dataset_dict[split].add_column('split', [split]*len(dataset_dict[split]))
+    # Concatenate all splits into one dataset
+    all_data = concatenate_datasets([dataset_dict[split] for split in dataset_dict.keys()])
+    # Convert to pandas DataFrame
+    df = all_data.to_pandas()
+    # Remove sequences with length less than 50
+    df['seq_length'] = df['seqs'].apply(len)
+    df = df[df['seq_length'] >= 50]
+    # Group by 'seqs' to find duplicates and average the labels
+    def aggregate_group(group):
+        avg_label = group['labels'].mean()
+        # Assign the sequence to the highest-precedence split it appears in
+        for p in precedence:
+            if p in group['split'].values:
+                selected_split = p
+                break
+        return pd.Series({'labels': avg_label, 'split': selected_split})
+    df_grouped = df.groupby('seqs').apply(aggregate_group).reset_index()
+    # Split the DataFrame back into the original splits without overlapping sequences
+    new_dataset_dict = DatasetDict()
+    for split in precedence:
+        df_split = df_grouped[df_grouped['split'] == split]
+        new_dataset_dict[split] = Dataset.from_pandas(df_split[['seqs', 'labels']], preserve_index=False)
+    return new_dataset_dict
+```
+
+From [DLKcat paper](https://www.nature.com/articles/s41929-022-00798-z)
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/62f2bd3bdb7cbd214b658c48/bC5PQ_O9_xKZzYEIYxuEM.png)
+