return feature_df

# Assume the columns are gene_product_id, go_term_id, and evidence_code gene_product_features = {}

# Convert to a DataFrame for easier handling feature_df = pd.DataFrame([ {'gene_product_id': gene_product_id, 'go_term_ids': go_term_ids} for gene_product_id, go_term_ids in gene_product_features.items() ])

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

# Further processing to create binary or count features # ...

Kg5 Da - File

return feature_df

# Assume the columns are gene_product_id, go_term_id, and evidence_code gene_product_features = {}

# Convert to a DataFrame for easier handling feature_df = pd.DataFrame([ {'gene_product_id': gene_product_id, 'go_term_ids': go_term_ids} for gene_product_id, go_term_ids in gene_product_features.items() ])

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

# Further processing to create binary or count features # ...