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An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis

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Schweikert,  G
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Widmer,  C
Friedrich Miescher Laboratory, Max Planck Society;

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Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Rätsch,  G
Friedrich Miescher Laboratory, Max Planck Society;

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Citation

Schweikert, G., Widmer, C., Schölkopf, B., & Rätsch, G. (2009). An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis. In D. Koller, D. Schuurmans, Y. Bengio, & L. Bottou (Eds.), Advances in neural information processing systems 21 (pp. 1433-1440). Red Hook, NY, USA: Curran.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-C46B-8
Abstract
We study the problem of domain transfer for a supervised classification task in mRNA splicing. We consider a number of recent domain transfer methods from machine learning, including some that are novel, and evaluate them on genomic sequence data from model organisms of varying evolutionary distance. We find that in cases where the organisms are not closely related, the use of domain adaptation methods can help improve classification performance.