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--- |
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dataset_info: |
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features: |
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|
- name: Entry |
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|
dtype: string |
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|
- name: seqs |
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|
dtype: string |
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|
- name: EC number |
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|
dtype: string |
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|
- name: Active site |
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|
dtype: string |
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|
- name: labels |
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|
dtype: int64 |
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|
splits: |
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|
- name: train |
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|
num_bytes: 50935453 |
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|
num_examples: 80212 |
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|
- name: valid |
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|
num_bytes: 686688 |
|
|
num_examples: 1000 |
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|
- name: test |
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|
num_bytes: 692134 |
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|
num_examples: 1000 |
|
|
download_size: 49792005 |
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|
dataset_size: 52314275 |
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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 |
|
|
path: data/valid-* |
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|
- split: test |
|
|
path: data/test-* |
|
|
--- |
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Uniprot (ec:*) AND (go_manual:*) on 5/30/25 |
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Remove any EC entries with '-' |
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Remove any EC entries with more than one EC number |
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Filter for sequences between 20 and 2048 |
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Cluster at 80% sequence similarity |
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docker run -v `pwd`:/data -w /data cd-hit cd-hit -i ec.fasta -o output_ec_80 -c 0.8 -n 3 -T 72 -M 512000 |
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Keep representative sequences |
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Keep EC numbers with 100 or more examples |
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Map each EC number to a unique integer |
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Remove any duplicates, favor entries with active site information |
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Shuffle |
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Split into 1000 valid and 1000 test randomly, make sure they all have active site info |
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13.8% of train entries have active site info |
