Mapping biological entities using the longest approximately common prefix method

Alex Rudniy, Min Song, James Geller

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


Background: The significant growth in the volume of electronic biomedical data in recent decades has pointed to the need for approximate string matching algorithms that can expedite tasks such as named entity recognition, duplicate detection, terminology integration, and spelling correction. The task of source integration in the Unified Medical Language System (UMLS) requires considerable expert effort despite the presence of various computational tools. This problem warrants the search for a new method for approximate string matching and its UMLS-based evaluation.Results: This paper introduces the Longest Approximately Common Prefix (LACP) method as an algorithm for approximate string matching that runs in linear time. We compare the LACP method for performance, precision and speed to nine other well-known string matching algorithms. As test data, we use two multiple-source samples from the Unified Medical Language System (UMLS) and two SNOMED Clinical Terms-based samples. In addition, we present a spell checker based on the LACP method.Conclusions: The Longest Approximately Common Prefix method completes its string similarity evaluations in less time than all nine string similarity methods used for comparison. The Longest Approximately Common Prefix outperforms these nine approximate string matching methods in its Maximum F1 measure when evaluated on three out of the four datasets, and in its average precision on two of the four datasets.

Original languageEnglish
Article number187
JournalBMC bioinformatics
Issue number1
Publication statusPublished - 2014 Jun 14

Bibliographical note

Funding Information:
This work was supported by the Bio and Medical Technology Development Program of the National Research Foundation funded by the Korean Ministry of Science and Technology (Grant No. 2013M3A9C4078138).

All Science Journal Classification (ASJC) codes

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics


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