Learning kernels from biological networks by maximizing entropy
Learning kernels from biological networks by maximizing entropy
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DOI：
10.1093/bioinformatics/bth906
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发表时间：
2004-08-04
期刊：
影响因子：
5.8
通讯作者：
Noble, William Stafford
中科院分区：
文献类型：
--
作者：
Tsuda, Koji;Noble, William Stafford
动机：扩散核是一种计算图中所有节点之间成对距离的通用方法，它基于每对节点之间的加权路径之和。这种技术已经成功地与基于核的学习方法结合使用，从几种类型的生物网络中得出推论。结果：我们表明，计算扩散核相当于最大化冯·诺伊曼熵，受到节点之间欧几里得距离总和的全局约束。这种全局约束允许两两距离的高方差。因此，我们提出了一种替代的局部约束扩散核，并证明了所得到的核允许更准确的支持向量机预测代谢和蛋白质-蛋白质相互作用网络中的蛋白质功能分类。
Motivation: 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.Results: 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.