Czarnecki Jan M, Shepherd Adrian J
School of Biosciences, Birkbeck, University of London, Malet Street, London, WC1E 7HX, UK.
Department of Biological Sciences and Institute of Structural and Molecular Biology, Birkbeck, University of London, Malet Street, London, WC1E 7HX, UK.
Methods Mol Biol. 2017;1526:139-158. doi: 10.1007/978-1-4939-6613-4_8.
Understanding metabolic pathways is one of the most important fields in bioscience in the post-genomic era, but curating metabolic pathways requires considerable man-power. As such there is a lack of reliable, experimentally verified metabolic pathways in databases and databases are forced to predict all but the most immediately useful pathways.Text-mining has the potential to solve this problem, but while sophisticated text-mining methods have been developed to assist the curation of many types of biomedical networks, such as protein-protein interaction networks, the mining of metabolic pathways from the literature has been largely neglected by the text-mining community. In this chapter we describe a pipeline for the extraction of metabolic pathways built on freely available open-source components and a heuristic metabolic reaction extraction algorithm.
在后基因组时代，理解代谢途径是生物科学中最重要的领域之一，但整理代谢途径需要大量人力。因此，数据库中缺乏可靠的、经过实验验证的代谢途径，数据库只能预测那些最直接有用的途径之外的其他途径。文本挖掘有潜力解决这个问题，然而，尽管已经开发出复杂的文本挖掘方法来辅助多种生物医学网络的整理，如蛋白质-蛋白质相互作用网络，但文本挖掘社区在很大程度上忽视了从文献中挖掘代谢途径。在本章中，我们描述了一个基于免费开源组件构建的代谢途径提取流程以及一种启发式代谢反应提取算法。
