An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis
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.
Cite
Text
Schweikert et al. "An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis." Neural Information Processing Systems, 2008.Markdown
[Schweikert et al. "An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis." Neural Information Processing Systems, 2008.](https://mlanthology.org/neurips/2008/schweikert2008neurips-empirical/)BibTeX
@inproceedings{schweikert2008neurips-empirical,
title = {{An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis}},
author = {Schweikert, Gabriele and Rätsch, Gunnar and Widmer, Christian K. and Schölkopf, Bernhard},
booktitle = {Neural Information Processing Systems},
year = {2008},
pages = {1433-1440},
url = {https://mlanthology.org/neurips/2008/schweikert2008neurips-empirical/}
}