callahantiff/PheKnowLator: v3.0.2

Release: v3.0.2

Website: https://github.com/callahantiff/PheKnowLator/wiki/v2.0.0 Data Access: Google Cloud Storage -- PheKnowLator Bucket Docker Container: DockerHub Dedicated Project Container PyPI: pkt-kg 3.0.2

Updated Jupyter Notebooks:

• notebooks/OWLNETS_Example_Application.ipynb
• notebooks/Data_Preparation.ipynb

Updated Scripts:

• builds/data_preprocessing.py
• pkt_kg/metadata.py
• pkt_kg/utils/kg_utils.py
• builds/data_to_download.txt
• pkt_kg/utils/data_utils.py
• tests/test_data_utils_downloading.py

Updates

• Addresses issue #118 (PR: #119) by patching the prior functionality related to obtaining labels and definitions from ontologies. Specifically, it now ensures that whenever possible the language encoding for these fields is English. Please see details below for information on how to address nodes containing foreign characters prior to this release.

  Solution for Builds Prior to v3.0.2 The (bad_node_patch.json) file contains a dictionary where the outer keys are the entity_uri and the puter values are another dictionary where the inner keys are label and description/definition and the inner values for these inner keys are the updated strings without foreign characters. An example of this dictionary is shown below:

  key = '<http://purl.obolibrary.org/obo/UBERON_0000468>'
  
  print(bad_node_patch[key])
  >>> {'label': 'multicellular organism', 'description/definition': 'Anatomical structure that is an individual member of a species and consists 
  of more than one cell.'}
  

  The code to identify the nodes with erroneous foreign characters is shown below:

  import re
  import pandas as pd
  
  # link to downloaded `NodeLabels.txt` file
  input_file = `'NodeLabels.txt'`
  
  # load data as Pandas DataFrame
  nodedf = pd.read_csv(input_file, sep='\t', header=0)
  
  # identify bad nodes and filter DataFrame so it only contains these rows
  nodedf['bad'] = nodedf['label'].apply(lambda x: re.search("[\u4e00-\u9FFF]", x) if not pd.isna(x) else None)
  nodedf_bad_nodes = nodedf[~pd.isna(nodedf['bad'])].drop_duplicates()