Learning in graphical models
Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
Gene functional classification from heterogeneous data
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Bioinformatics: the machine learning approach
Bioinformatics: the machine learning approach
Application of Cascade Correlation Networks for Structures toChemistry
Applied Intelligence
The em algorithm for kernel matrix completion with auxiliary data
The Journal of Machine Learning Research
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Learning the Kernel with Hyperkernels
The Journal of Machine Learning Research
Learning the Kernel Function via Regularization
The Journal of Machine Learning Research
Metric Learning for Text Documents
IEEE Transactions on Pattern Analysis and Machine Intelligence
Transductive learning with EM algorithm to classify proteins based on phylogenetic profiles
International Journal of Data Mining and Bioinformatics
A general framework for adaptive processing of data structures
IEEE Transactions on Neural Networks
Guest editorial: Computational intelligence and machine learning in bioinformatics
Artificial Intelligence in Medicine
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Objective: Modeling phylogenetic interactions is an open issue in many computational biology problems. In the context of gene function prediction we introduce a class of kernels for structured data leveraging on a hierarchical probabilistic modeling of phylogeny among species. Methods and materials: We derive three kernels belonging to this setting: a sufficient statistics kernel, a Fisher kernel, and a probability product kernel. The new kernels are used in the context of support vector machine learning. The kernels adaptivity is obtained through the estimation of the parameters of a tree structured model of evolution using as observed data phylogenetic profiles encoding the presence or absence of specific genes in a set of fully sequenced genomes. Results: We report results obtained in the prediction of the functional class of the proteins of the budding yeast Saccharomyces cerevisae which favorably compare to a standard vector based kernel and to a non-adaptive tree kernel function. A further comparative analysis is performed in order to assess the impact of the different components of the proposed approach. Conclusions: We show that the key features of the proposed kernels are the adaptivity to the input domain and the ability to deal with structured data interpreted through a graphical model representation.