Detecting duplicate biological entities using Shortest Path Edit Distance.
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.
Rudniy
Alex
A
Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102, USA. rudniy@njit.edu
Song
Min
M
Geller
James
J
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods