An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis

Part of Advances in Neural Information Processing Systems 21 (NIPS 2008)

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Gabriele Schweikert, Gunnar Rätsch, Christian Widmer, Bernhard Schölkopf


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.