Performance of ENQUIRE’s gene normalization algorithm.

Precision, recall, and their harmonic mean (F1) are based on 479 abstracts from the NLM-Gene corpus containing at least one mention to a H. sapiens or M. musculus gene. Different gene normalization methods were evaluated by adding or removing filters for excluding predicted cell entities (en_ner_jnlpba_md) and ambiguous abbreviation-definition pairs (Schwartz-Hearst). Gene mentions contained in cell entities such as “CD8+ T cell” are true positives in the NLM-Gene corpus. Text spans tagged as cell entities by the en_ner_jnlpba model are removed without being processed by the tokenizer module. Maximum RAM usage is measured as resident set size (RSS). Estimated time in seconds per abstract (sec/abstract) also accounts for loading the gene alias lookup table and machine learning models. The best values for each parameter setting are highlighted in bold.