Assessment of approximate string matching in a biomedical text retrieval problem

作者：Wang JF; Li ZR; Cai CZ; Chen YZ^*

来源：Computers in Biology and Medicine, 2005, 35(8): 717-724.

DOI：10.1016/j.compbiomed.2004.06.002

• bioinformatics
• biomedical
• data mining
• dynamic programming
• herb
• herbal medicine
• literature
• literature search
• medicine
• medicinal plant
• medinformatics
• plant
• Smith-Waterman algorithm
• text
• text matching
• word
• word matching

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 of medicinal herbs collected from herbal medicine literatures are matched with those from medicinal 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.

• 出版日期2005-10
• 单位四川大学; 重庆大学