Learning kernels from biological networks by maximizing entropy | Zendy

Koji Tsuda | Zendy; William Stafford Noble | Zendy

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Learning kernels from biological networks by maximizing entropy

Author(s) -

Koji Tsuda,

William Stafford Noble

Publication year - 2004

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bth906

Subject(s) - pairwise comparison , kernel (algebra) , computer science , kernel method , graph kernel , graph , entropy (arrow of time) , geometric networks , algorithm , theoretical computer science , kernel embedding of distributions , artificial intelligence , mathematics , support vector machine , discrete mathematics , complex network , physics , quantum mechanics , world wide web

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.

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