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首页> 外文期刊>Journal of Bioinformatics and Computational Biology >Knowledge-based generalization of metabolic networks: A practical study
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Knowledge-based generalization of metabolic networks: A practical study

机译：基于知识的代谢网络一般化：实践研究

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摘要

The complex process of genome-scale metabolic network reconstruction involves semi-automatic reaction inference, analysis, and refinement through curation by human experts. Unfortunately, decisions by experts are hampered by the complexity of the network, which can mask errors in the inferred network. In order to aid an expert in making sense out of the thousands of reactions in the organism's metabolism, we developed a method for knowledge-based generalization that provides a higher-level view of the network, highlighting the particularities and essential structure, while hiding the details. In this study, we show the application of this generalization method to 1,286 metabolic networks of organisms in Path2Models that describe fatty acid metabolism. We compare the generalised networks and show that we successfully highlight the aspects that are important for their curation and comparison.
机译：基因组规模的代谢网络重建的复杂过程涉及半自动反应的推断，分析以及由人类专家精心策划的精炼。不幸的是，专家的决定因网络的复杂性而受到阻碍，因为网络的复杂性会掩盖推断出的网络中的错误。为了帮助专家从生物的新陈代谢中了解数千个反应，我们开发了一种基于知识的概括方法，该方法提供了网络的更高层次的视图，突出显示了特殊性和基本结构，同时隐藏了细节。在这项研究中，我们展示了该泛化方法在描述脂肪酸代谢的Path2Models中对1286个生物代谢网络的应用。我们比较了广义网络，并表明我们成功地突出了对策展和比较很重要的方面。

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