Detecting duplicate biological entities using Shortest Path Edit Distance

Authors:
Alex Rudniy;Min Song;James Geller
Affiliations:
Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102, USA.;Department of Information Systems, New Jersey Institute of Technology, Newark, NJ 07102, USA.;Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102, USA
Venue:
International Journal of Data Mining and Bioinformatics
Year:
2010

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Abstract

Duplicate entity detection in biological data is an important research task. In this paper, we propose a novel and context-sensitive Shortest Path Edit Distance (SPED) extending and supplementing our previous work on Markov Random Field-based Edit Distance (MRFED). SPED transforms the edit distance computational problem to the calculation of the shortest path among two selected vertices of a graph. We produce several modifications of SPED by applying Levenshtein, arithmetic mean, histogram difference and TFIDF techniques to solve subtasks. We compare SPED performance to other well-known distance algorithms for biological entity matching. The experimental results show that SPED produces competitive outcomes.