Propagating distributions on a hypergraph by dual information regularization
ICML '05 Proceedings of the 22nd international conference on Machine learning
Analysis of protein-protein interaction networks using random walks
Proceedings of the 5th international workshop on Bioinformatics
Probabilistic relaxation labelling using the Fokker-Planck equation
Pattern Recognition
Semi-supervised Classification from Discriminative Random Walks
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Protein functional class prediction with a combined graph
Expert Systems with Applications: An International Journal
On Pairwise Kernels: An Efficient Alternative and Generalization Analysis
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Relational learning via latent social dimensions
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning Kernel Matrix from Gene Ontology and Annotation Data for Protein Function Prediction
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
On multiple kernel learning with multiple labels
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Probabilistic relaxation labeling by Fokker-Planck diffusion on a graph
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
Gene function prediction with gene interaction networks: a context graph kernel approach
IEEE Transactions on Information Technology in Biomedicine
Link prediction via matrix factorization
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Leveraging social media networks for classification
Data Mining and Knowledge Discovery
Algorithms for detecting significantly mutated pathways in cancer
RECOMB'10 Proceedings of the 14th Annual international conference on Research in Computational Molecular Biology
Predicting Protein Function by Multi-Label Correlated Semi-Supervised Learning
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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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. Availability: Supplementary results and data are available at noble.gs.washington.edu/proj/maxent