Mapping biological entities using the longest approximately common prefix method

• Published in: BMC bioinformatics Vol. 15; no. 1; p. 187
• Main Authors: Rudniy, Alex, Song, Min, Geller, James
• Format: Journal Article
• Language: English
• Published: London BioMed Central 14.06.2014; BioMed Central Ltd
• Subjects: Algorithms; Approximate string matching; Bioinformatics; Biomedical and Life Sciences; Computational Biology - methods; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Computer Graphics; Humans; Knowledge-based analysis; Language; Life Sciences; Medical; Methodology; Methodology Article; Microarrays; Natural Language Processing; Searching; Similarity; Strings; Tasks; Terminology as Topic; Unified Medical Language System; Unified Modeling Language; Common Prefix; Unify Medical Language System; Average Precision; String Similarity; Histogram Intersection
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/1471-2105-15-187

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 F 1 measure when evaluated on three out of the four datasets, and in its average precision on two of the four datasets.