Assessment of approximate string matching in a biomedical text retrieval problem
J.F. Wanga, Z.R. Lia, Chunli Caia -, Yiqiang Chena · 2005
Text-based search is widely used for biomedical data mining and knowledge discovery. Character errors in literatures affect the accuracy of data mining. Methods for solving this problem are being explored. This work tests the usefulness of the Smith–Waterman algorithm with affine gap penalty as a method for biomedical literature retrieval.Names ofmedicinal herbs collected fromherbalmedicine literatures arematchedwith those frommedicinal chemistry literatures by using this algorithm at different string identity levels (80–100%). The optimum performance is at string identity of 88%, at which the recall and precision are 96.9% and 97.3%, respectively. Our study suggests that the Smith–Waterman algorithm is useful for improving the success rate of biomedical text retrieval. 2004 Elsevier Ltd. All rights reserved.