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A Comparative Study of Tree Generative Kernels for Gene Function Prediction

Nicotra, Luca and Micheli, Alessio and Starita, Antonina (2007) A Comparative Study of Tree Generative Kernels for Gene Function Prediction. Technical Report del Dipartimento di Informatica . Università di Pisa, Pisa, IT.

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    In this report we perform a comparative study of kernel functions defined on generative models with the goal to embed phylogenetic information into a discriminative learning approach. We describe three generative tree kernels: a sufficient statistics kernel, a Fisher kernel, and a probability product kernel; their key features are the adaptivity to the input domain and the ability to deal with structured data. In particular, kernel adaptivity is obtained through the estimation of the parameters of a tree structured model of evolution from an input domain of phylogenetic profiles encoding the presence or absence of specific proteins in a set of fully sequenced genomes. We report results obtained in the prediction of the functional class of the proteins of the yeast S. Cervisae together with comparisons with a standard vector based kernel and with a non-adaptive tree kernel function. To further analyze the impact of the discriminative learning phase, and to provide an assessment of the information retained by the learned generative models we apply them directly to classification through log-odds. Finally, the advantage achieved through adaptivity for two of the new kernels is assessed through a comparison with similar kernels based on randomly initialized generative models where no learning is performed, and to kernels where parameters are set only on the base of biological considerations.

    Item Type: Book
    Uncontrolled Keywords: Kernels for structures, phylogenetic profiles, Fisher kernel, probability product kernel, gene function prediction, Bayesian networks
    Subjects: Area01 - Scienze matematiche e informatiche > INF/01 - Informatica
    Divisions: Dipartimenti (until 2012) > DIPARTIMENTO DI INFORMATICA
    Depositing User: dott.ssa Sandra Faita
    Date Deposited: 09 Dec 2014 13:20
    Last Modified: 09 Dec 2014 13:20
    URI: http://eprints.adm.unipi.it/id/eprint/2188

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