Tsuda Koji, Noble William Stafford
Max Planck Institute for Biological Cybernetics, Tübingen, Germany.
Bioinformatics. 2004 Aug 4;20 Suppl 1:i326-33. doi: 10.1093/bioinformatics/bth906.
The diffusion kernel is a general method for computing pairwise distances among all nodes in a graph, based on the sum of weighted paths between each pair of nodes. This technique has been used successfully, in conjunction with kernel-based learning methods, to draw inferences from several types of biological networks.
We show that computing the diffusion kernel is equivalent to maximizing the von Neumann entropy, subject to a global constraint on the sum of the Euclidean distances between nodes. This global constraint allows for high variance in the pairwise distances. Accordingly, we propose an alternative, locally constrained diffusion kernel, and we demonstrate that the resulting kernel allows for more accurate support vector machine prediction of protein functional classifications from metabolic and protein-protein interaction networks.
Supplementary results and data are available at noble.gs.washington.edu/proj/maxent
扩散核是一种基于图中每对节点之间加权路径之和来计算所有节点之间成对距离的通用方法。该技术已与基于核的学习方法成功结合，用于从多种类型的生物网络中进行推理。
我们表明，计算扩散核等同于在节点之间欧几里得距离之和的全局约束下最大化冯·诺依曼熵。这种全局约束允许成对距离存在高方差。因此，我们提出了一种替代的、局部约束的扩散核，并证明所得核能够从代谢和蛋白质 - 蛋白质相互作用网络中对蛋白质功能分类进行更准确的支持向量机预测。
补充结果和数据可在noble.gs.washington.edu/proj/maxent获取
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