metadata
dataset_info:
features:
- name: seqs
dtype: string
- name: labels
dtype: float64
splits:
- name: train
num_bytes: 2933951
num_examples: 6837
- name: valid
num_bytes: 217038
num_examples: 498
- name: test
num_bytes: 204262
num_examples: 469
download_size: 2178499
dataset_size: 3355251
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
DLKcat (BRENDA and SABIO-RK) with splits from Biomap, 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
