An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence 
Analysis

Schweikert, G; Widmer, C; Schölkopf, B; Rätsch, G

Item

ITEM ACTIONSEXPORT

Add to Basket

Local TagsRelease HistoryDetailsSummary

Released

Conference Paper

An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis

MPS-Authors

/persons/resource/persons84204

Schweikert, G
Department Molecular Biology, Max Planck Institute for Developmental Biology, Max Planck Society;

External Resource

https://proceedings.neurips.cc/paper/2008/file/087408522c31eeb1f982bc0eaf81d35f-Paper.pdf
(Publisher version)

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

There are no public fulltexts stored in PuRe

Supplementary Material (public)

There is no public supplementary material available

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: https://hdl.handle.net/21.11116/0000-0010-3E45-C

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.