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 adap-tation methods can help improve classification performance.
|Title of host publication||Advances in Neural Information Processing Systems 21 (NIPS 2008)|
|Number of pages||14|
|Publication status||Published - 2008|