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Filter a subnetwork by contextual relevance

filterSubnetworkByContext.Rd

Fetches PubMed abstracts for evidence PMIDs, scores each abstract against a user-supplied query, and returns only the nodes, edges, and evidence rows whose abstracts meet the scoring cutoff.

Usage

filterSubnetworkByContext(
  nodes,
  edges,
  query,
  cutoff = NULL,
  method = c("tag_count", "cosine")
)

Arguments

nodes

A dataframe of network nodes.

edges

A dataframe of network edges with columns: source, target, interaction, site, evidenceLink, stmt_hash.

query

For method = "tag_count": a character vector of tags, e.g. c("CHEK1", "DNA damage", "DNA damage repair"). For method = "cosine": a single character string.

cutoff

Numeric threshold applied to the chosen scoring method.

  • "tag_count": integer >= 0; abstracts must contain at least this many tags. Max possible value is length(query). Default 1.

  • "cosine": numeric in [-1, 1]; abstracts must score >= this value. Default 0.10.

method

One of "tag_count" (default) or "cosine".

Value

A named list with three elements:

nodes

Filtered nodes dataframe (only nodes present in kept edges)

edges

Filtered edges dataframe

evidence

Dataframe with columns: source, target, interaction, site, evidenceLink, stmt_hash, text, pmid, score. The score column contains tag counts (integer) or cosine similarities (numeric) depending on the method used.

Details

Two scoring methods are available, controlled by the method argument:

"tag_count" (default)

Counts how many tags from query appear as substrings in the abstract (case-insensitive). The score for each abstract is an integer in [0, length(query)]. Set cutoff to the minimum number of tags that must appear - e.g. cutoff = 2 keeps abstracts that mention at least 2 of your tags. query must be a character vector of tags when using this method.

"cosine"

Scores abstracts using TF-IDF cosine similarity against query. Scores are in [-1, 1] (in practice [0, 1] for text). Set cutoff to a decimal threshold - e.g. cutoff = 0.10. query should be a single character string; expand it with synonyms and related terms for better recall under exact token matching.