Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Center CLICK: A Clustering Algorithm with Applications to Gene Expression Analysis
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Analyzing time series gene expression data
Bioinformatics
Protein function prediction via graph kernels
Bioinformatics
Kernel methods for predicting protein--protein interactions
Bioinformatics
Shortest-Path Kernels on Graphs
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Extracting Dynamics from Static Cancer Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Discovering relations among GO-annotated clusters by Graph Kernel methods
ISBRA'07 Proceedings of the 3rd international conference on Bioinformatics research and applications
Evaluating graph kernel methods for relation discovery in GO-annotated clusters
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III
Contrast mining from interesting subgroups
Bisociative Knowledge Discovery
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The application of various clustering techniques for large-scale gene-expression measurement experiments is a well-established method in bioinformatics. Clustering is also usually accompanied by functional characterization of gene sets by assessing statistical enrichments of structured vocabularies, such as the gene ontology (GO) [Gene Ontology Consortium. The gene ontology (GO) project in 2006. Nucleic Acids Research (Database issue), vol. 34; 2006. p. D322-6]. If different clusters are generated for correlated experiments, a machine learning step termed cluster meta-analysis may be performed, in order to discover relations among the components of such sets. Several approaches have been proposed: in particular, kernel methods may be used to exploit the graphical structure of typical ontologies such as GO. Following up the formulation of such approach [Merico D, Zoppis I, Antoniotti M, Mauri G. Evaluating graph kernel methods for relation discovery in GO-annotated clusters. In: KES-2007/WIRN-2007, Part IV, Lecture notes in artificial intelligence, vol. 4694. Berlin: Springer; 2007. p. 892-900; Zoppis I, Merico D, Antoniotti M, Mishra B, Mauri G. Discovering relations among GO-annotated clusters by graph kernel methods. In: Proceedings of the 2007 international symposium on bioinformatics research and applications. Lecture notes in computer science, vol. 4463. Berlin: Springer; 2007], in this paper we discuss, from an information-theoretic point of view, further results about its applicability and its performance.