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--- |
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dataset_info: |
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features: |
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- name: seqs |
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dtype: string |
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- name: labels |
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dtype: float64 |
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splits: |
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- name: train |
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num_bytes: 2933951 |
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num_examples: 6837 |
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- name: valid |
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num_bytes: 217038 |
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num_examples: 498 |
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- name: test |
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num_bytes: 204262 |
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num_examples: 469 |
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download_size: 2178499 |
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dataset_size: 3355251 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: valid |
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path: data/valid-* |
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- split: test |
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path: data/test-* |
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--- |
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[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. |
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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. |
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Processing: |
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``` |
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import pandas as pd |
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from datasets import Dataset, DatasetDict, concatenate_datasets |
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def process_dataset(dataset_dict): |
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precedence = ['train', 'valid', 'test'] |
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# Add a 'split' column to each dataset |
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for split in dataset_dict.keys(): |
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dataset_dict[split] = dataset_dict[split].add_column('split', [split]*len(dataset_dict[split])) |
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# Concatenate all splits into one dataset |
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all_data = concatenate_datasets([dataset_dict[split] for split in dataset_dict.keys()]) |
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# Convert to pandas DataFrame |
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df = all_data.to_pandas() |
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# Remove sequences with length less than 50 |
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df['seq_length'] = df['seqs'].apply(len) |
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df = df[df['seq_length'] >= 50] |
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# Group by 'seqs' to find duplicates and average the labels |
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def aggregate_group(group): |
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avg_label = group['labels'].mean() |
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# Assign the sequence to the highest-precedence split it appears in |
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for p in precedence: |
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if p in group['split'].values: |
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selected_split = p |
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break |
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return pd.Series({'labels': avg_label, 'split': selected_split}) |
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df_grouped = df.groupby('seqs').apply(aggregate_group).reset_index() |
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# Split the DataFrame back into the original splits without overlapping sequences |
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new_dataset_dict = DatasetDict() |
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for split in precedence: |
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df_split = df_grouped[df_grouped['split'] == split] |
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new_dataset_dict[split] = Dataset.from_pandas(df_split[['seqs', 'labels']], preserve_index=False) |
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return new_dataset_dict |
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``` |
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From [DLKcat paper](https://www.nature.com/articles/s41929-022-00798-z) |
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