<jupyter_start><jupyter_text>Adding Literature Bias to Data-Driven OntologiesThis notebook illustrates how to use BEL networks to modify the generation of data driven ontologies. It uses several resources:  Notebook Provenance  Imports<jupyter_code>import os
import sys
import time

import pybel
import ddot

from ddot import Ontology<jupyter_output><empty_output><jupyter_text>Environment<jupyter_code>print(sys.version)
print(time.asctime())
print(pybel.constants.VERSION)<jupyter_output>0.11.0<jupyter_text>Data Acquisition  Parsimonious Composite Network (PCNet) PCNet is a union of molecular interactions across 21 networks that appear in at least 2 of the networks investigated by Huang and Carlin *et al.*. It has the NDEx UUID of `f93f402c-86d4-11e7-a10d-0ac135e8bacf`, which could be replaced with other networks for similar analysis.<NAME>., <NAME>., <NAME>., <NAME>., <NAME>., <NAME>., & <NAME>. (2018). [Systematic Evaluation of Molecular Networks for Discovery of Disease Genes](https://doi.org/10.1016/j.cels.2018.03.001). Cell Systems, 6(4), 484–495.e5.<jupyter_code>ndex_uuid = 'f93f402c-86d4-11e7-a10d-0ac135e8bacf'
%%time

ontology = Ontology.from_ndex(
    ndex_server='http://ndexbio.org',
    ndex_uuid=ndex_uuid,
)<jupyter_output>
{'fraction_non_alphanumeric': 0.10383386581469649, 'fraction_numerical': 0.05670926517571885, 'mean_word_length': 5.961111111111111, 'pattern_counts': {'":': 0, '<': 18, '<?xml version=': 0, '>': 18, 'https://': 1, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '29183', 'license': 'MIT', 'n_tokens_mistral': 484, 'n_tokens_neox': 427, 'n_words': 119, 'path': 'examples/Adding Literature Bias to DDOT.ipynb', 'repo_name': 'cthoyt/ddot', 'size': '1282'}