利用代谢网络和支持向量机的结构特性分析和预测营养需求。

Takeyuki Tamura, Nils Christian, Kazuhiro Takemoto, Oliver Ebenhöh, Tatsuya Akutsu
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引用次数: 0

摘要

基因组尺度代谢网络的图表示特性已经得到了广泛的研究。然而,这些网络的结构性质和功能性质之间的关系仍然非常不清楚。在本文中,我们将生物体的营养需求作为一种功能属性,并研究了代谢网络图表示与结构属性的关系。为了检验这种关系,我们研究了利用支持向量机从结构属性(度指数、边缘密度、聚类系数、度中心性、亲密中心性、中间中心性和特征向量中心性)预测营养需求的程度。此外,我们还研究了哪些特性对营养需求有影响。
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Analysis and prediction of nutritional requirements using structural properties of metabolic networks and support vector machines.

Properties of graph representation of genome scale metabolic networks have been extensively studied. However, the relationship between these structural properties and functional properties of the networks are still very unclear. In this paper, we focus on nutritional requirements of organisms as a functional property and study the relationship with structural properties of a graph representation of metabolic networks. In order to examine the relationship, we study to what extent the nutritional requirements can be predicted by using support vector machines from structural properties, which include degree exponent, edge density, clustering coefficient, degree centrality, closeness centrality, betweenness centrality and eigenvector centrality. Furthermore, we study which properties are influential to the nutritional requirements.

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