Shortest path edit distance for detecting duplicate biological entities

Authors:
Alex Rudniy;Min Song;James Geller
Affiliations:
New Jersey Institute of Technology, Newark, NJ;New Jersey Institute of Technology, Newark, NJ;New Jersey Institute of Technology, Newark, NJ
Venue:
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
Year:
2010

Citing 5
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Abstract

This paper presents a novel and context-sensitive Shortest Path Edit Distance (SPED) applied to duplicate entity detection in biological data. SPED is an extension of Markov Random Field-based Edit Distance. It transforms the edit distance computational problem to the calculation of the shortest path among two selected vertices of a graph. The experimental results show that SPED produces competitive outcomes. SoftSPED, the combination of SPED with TFIDF, achieves superior performance in most cases.