Background: Increasingly biological text mining research is focusing on the extraction of complex relationshipsudrelevant to the construction and curation of biological networks and pathways. However, one important category ofudpathway—metabolic pathways—has been largely neglected.udHere we present a relatively simple method for extracting metabolic reaction information from free text that scoresuddifferent permutations of assigned entities (enzymes and metabolites) within a given sentence based on the presenceudand location of stemmed keywords. This method extends an approach that has proved effective in the context of theudextraction of protein–protein interactions.ududResults: When evaluated on a set of manually-curated metabolic pathways using standard performance criteria, ourudmethod performs surprisingly well. Precision and recall rates are comparable to those previously achieved for theudwell-known protein-protein interaction extraction task.ududConclusions: We conclude that automated metabolic pathway construction is more tractable than has often beenudassumed, and that (as in the case of protein–protein interaction extraction) relatively simple text-mining approaches can prove surprisingly effective. It is hoped that these results will provide an impetus to further research and act as a useful benchmark for judging the performance of more sophisticated methods that are yet to be developed.
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机译：背景：越来越多的生物文本挖掘研究集中在提取与构建和管理生物网络和路径无关的复杂关系上。但是， udpathway的一个重要类别（代谢途径）已被大大忽略。 ud此处，我们提出了一种相对简单的方法，用于从自由文本中提取代谢反应信息，该分数对给定句子中指定实体（酶和代谢物）的不同排列评分根据词干关键字的存在 udand位置。该方法扩展了在蛋白质-蛋白质相互作用的分离中被证明有效的方法。 ud ud结果：当使用标准性能标准对一组手动固化的代谢途径进行评估时，我们的 udmethod表现出色。精密度和召回率与以前的“众所周知的蛋白质-蛋白质相互作用提取任务”相当。 ud ud结论：我们得出结论，自动代谢途径的构建比通常的/易处理的更容易处理，并且（如蛋白质-蛋白质相互作用提取的案例）相对简单的文本挖掘方法可以证明出奇的有效。希望这些结果将为进一步的研究提供动力，并为判断尚待开发的更复杂方法的性能提供有用的基准。
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