Kg5: Da File

for index, row in kg5_data.iterrows(): gene_product_id = row['gene_product_id'] go_term_id = row['go_term_id']

# Further processing to create binary or count features # ... kg5 da file

# Assume the columns are gene_product_id, go_term_id, and evidence_code gene_product_features = {} for index, row in kg5_data

return feature_df

# Usage features = generate_features('path/to/kg5_file.kg5') features.to_csv('generated_features.csv', index=False) kg5 da file

gene_product_features[gene_product_id].append(go_term_id)