Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Analyzing time series gene expression data
Bioinformatics
Bioinformatics
Protein function prediction via graph kernels
Bioinformatics
Kernel methods for predicting protein--protein interactions
Bioinformatics
An application of kernel methods to gene cluster temporal meta-analysis
Computers and Operations Research
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 biological interpretation of large-scale gene expression data is one of the challenges in current bioinformatics. The state-of-theart approach is to perform clustering and then compute a functional characterization via enrichments by Gene Ontology terms [1]. To better assist the interpretation of results, it may be useful to establish connections among different clusters. This machine learning step is sometimes termed cluster meta-analysis, and several approaches have already been proposed; in particular, they usually rely on enrichments based on flat lists of GO terms. However, GO terms are organized in taxonomical graphs, whose structure should be taken into account when performing enrichment studies. To tackle this problem, we propose a kernel approach that can exploit such structured graphical nature. Finally, we compare our approach against a specific flat list method by analyzing the cdc15- subset of the well known Spellman's Yeast Cell Cycle dataset [2].