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An empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis
Gabriele B Schweikert · Christian Widmer · Bernhard Schölkopf · Gunnar Rätsch

Mon Dec 08 08:45 PM -- 12:00 AM (PST) @

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.

Author Information

Gabriele B Schweikert (MPI Tuebingen)
Christian Widmer (MSKCC)
Bernhard Schölkopf (MPI for Intelligent Systems, Tübingen)

Bernhard Scholkopf received degrees in mathematics (London) and physics (Tubingen), and a doctorate in computer science from the Technical University Berlin. He has researched at AT&T Bell Labs, at GMD FIRST, Berlin, at the Australian National University, Canberra, and at Microsoft Research Cambridge (UK). In 2001, he was appointed scientific member of the Max Planck Society and director at the MPI for Biological Cybernetics; in 2010 he founded the Max Planck Institute for Intelligent Systems. For further information, see www.kyb.tuebingen.mpg.de/~bs.

Gunnar Rätsch (ETHZ)

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