Heuristic parallelizable algorithm for similarity based biosystems comparison

Abstract

Biosystem comparison plays a major role in system biology. Similar biosystems are identified based on the similarity of species naming. Since the species naming does not follow a standard nomenclature, similarity is not easy to formalize. A single metabolite can have different name strings that vary slightly in pattern. Several algorithms have been designed to find similarity between two species using different measures. However, these algorithms failed to achieve good performance in biological species similarity checking due to failure to account for important facts about biological name analysis. We developed ParaABioS, a heuristic, intuitive algorithm for biosystem similarity evaluation. This algorithm integrates sub-name analysis and a symbol management strategy that conserves species name specifications. ParaABioS provides similarity checking between two names that consumes the time where k is the number of sub names in worst case. It is implemented in Java and parallelized on a Texas Advanced Computing Center TACC high performance computing (HPC) server and accessed through the iPlant Collaborative Foundation API in order to compare large models. It is available online and also through the BioExtract Server workflow management system (WMS).

Automatic biosystems comparison using semantic and name similarity

“…Name similarity is defined using minimum edit distance based on the ParaABioS [21]. Meaning similarity compares their annotated resource URI.…”

“…However, any of the existing edit distance algorithms (simplest version or weighted version) cannot be applied directly to compare metabolite names. Therefore, we chose an algorithm ParaABioS [21] that includes several special facts for metabolite names analysis such as sub-name comparison, number format management, abbreviation, and special character focusing, etc.…”