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An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis
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.
@inproceedings{5401, title = {An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis}, journal = {Advances in neural information processing systems 21 : 22nd Annual Conference on Neural Information Processing Systems 2008}, booktitle = {Advances in neural information processing systems 21}, 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.}, pages = {1433-1440}, editors = {D Koller and D Schuurmans and Y Bengio and L Bottou}, publisher = {Curran}, organization = {Max-Planck-Gesellschaft}, institution = {NIPS 2008}, school = {Biologische Kybernetik}, address = {Red Hook, NY, USA}, month = jun, year = {2009}, slug = {5401}, author = {Schweikert, G. and Widmer, C. and Sch{\"o}lkopf, B. and R{\"a}tsch, G.}, month_numeric = {6} }