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Scripts to reproduce results reported in FamPlex manuscript

This repository is organized into six folders corresponding to each stage of constructing and evaluating the FamPlex resource.

Step 1: Get list of genes and corresponding PMIDs

  • Download Reactome signaling proteins list (see step1_genes_pmids/signaling_proteins_readme.txt)
    • output: signaling_proteins.tsv
  • Run process_proteins.py to get a gene list for literature search
    • For each protein, get gene name.
    • output: signaling_genes.txt
    • Get genes identified in famplex/relations.csv (which included families from BEL and from the Ras family).
    • Combine the two gene lists
    • output: combined_genes.txt.
  • Run get_pmids.py to get a list of PMIDs from gene list
    • For each gene in combined_genes.txt, get papers curated from Entrez gene.
    • Save dict mapping gene to list of papers
    • output: combined_genes_to_pmids.pkl
    • Make set of unique PMIDs, save
    • output: combined_pids.txt

Step 2: Build REACH without FamPlex and read PMIDs

  • Build REACH without FamPlex
  • output: combined_genes_no_famplex_stmts.pkl (the pickle file contains a dictionary mapping each PMID to a list of Statements)

Step 3: Shuffle the PMIDs and subsample the data into training and test sets

  • 80% of papers to training set, 20% of papers to test set
  • get_training_test_stmts.py
    • output: training_pmid_stmts.pkl
    • output: test_pmid_stmts.pkl
    • output: training_pmids.txt
    • output: test_pmids.txt

Step 4: stmt_entity_stats

  • Run get_agents_for_evaluation.py. Creates random sample of agents from training (or test) set for evaluation.
    • output: training_agents_sample.csv
    • output: training_agents.csv
    • output: training_ungrounded.csv
    • output: training_agent_distribution.pdf
    • output: training_ungrounded_distribution.pdf
  • Open training_agents_sample_curated.csv in Excel to curate
    • Curate agents. Code:
      • P: protein
      • F: family
      • C: complex
      • X: complex of families
      • S: small molecule
      • B: biological process
      • U: unknown/other
      • M: microRNA
  • For ease of curation, run generate_agent_links.py to create an HTML table to check groundings in different databases.
  • Same for test set.

Step 5: Curate grounding map

  • texts_for_gene.py helps in finding lexical synonyms for unmapped families.
  • Fraction of most frequent agents that in clude family or complex
    • Fraction of mentions
  • Fraction of most frequent ungrounded that include family or complex
    • Fraction of ungrounded mentions
  • Table with 3 columns Entity, Frequency, Category (F, C, X, or empty), Curator

Step 6: Evaluate FamPlex resource

  • Creates a plot of frequency of groundings to each entity

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