Qi Xinjian, Ozsoyoglu Z Meral, Ozsoyoglu Gultekin
Department of Electrical Engineering and Computer Science, Case Western Reserve University, United States.
Methods. 2014 Oct 1;69(3):282-97. doi: 10.1016/j.ymeth.2014.06.007. Epub 2014 Jul 24.
Comparing and identifying matching metabolites, reactions, and compartments in genome-scale reconstructed metabolic networks can be difficult due to inconsistent naming in different networks. In this paper, we propose metabolite and reaction matching techniques for matching metabolites and reactions in a given metabolic network to metabolites and reactions in another metabolic network. We employ a variety of techniques that include approximate string matching, similarity score functions and multi-step filtering techniques, all enhanced by a set of rules based on the underlying metabolic biochemistry. The proposed techniques are evaluated by an empirical study on four pairs of metabolic networks, and significant accuracy gains are achieved using the proposed metabolite and reaction identification techniques.
由于不同网络中命名不一致,在基因组规模重建的代谢网络中比较和识别匹配的代谢物、反应和区室可能会很困难。在本文中,我们提出了代谢物和反应匹配技术,用于将给定代谢网络中的代谢物和反应与另一个代谢网络中的代谢物和反应进行匹配。我们采用了多种技术,包括近似字符串匹配、相似性评分函数和多步过滤技术,所有这些技术都通过一组基于基础代谢生物化学的规则得到了增强。通过对四对代谢网络的实证研究对所提出的技术进行了评估,使用所提出的代谢物和反应识别技术取得了显著的准确性提升。