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
Discovering relations among GO-annotated clusters by Graph Kernel methods
ISBRA'07 Proceedings of the 3rd international conference on Bioinformatics research and applications
An application of kernel methods to gene cluster temporal meta-analysis
Computers and Operations Research
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The application of various clustering techniques for large-scale gene-expression measurement experiments is an 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) [1]. If different cluster sets 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 for this step: 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 [2], in this paper we present and discuss further results about its applicability and its performance, always in the context of the well known Spellman's Yeast Cell Cycle dataset [3].