Simple ensemble methods are competitive with state-of-the-art data integration methods for gene function prediction

Matteo Ré, Giorgio Valentini
Proceedings of the third International Workshop on Machine Learning in Systems Biology, PMLR 8:98-111, 2009.

Abstract

Several works showed that biomolecular data integration is a key issue to improve the prediction of gene functions. Quite surprisingly only little attention has been devoted to data integration for gene function prediction through ensemble methods. In this work we show that relatively simple ensemble methods are competitive and in some cases are also able to outperform state-of-the-art data integration techniques for gene function prediction.

Cite this Paper

BibTeX
@InProceedings{pmlr-v8-re10a, title = {Simple ensemble methods are competitive with state-of-the-art data integration methods for gene function prediction}, author = {Ré, Matteo and Valentini, Giorgio}, booktitle = {Proceedings of the third International Workshop on Machine Learning in Systems Biology}, pages = {98--111}, year = {2009}, editor = {Džeroski, Sašo and Guerts, Pierre and Rousu, Juho}, volume = {8}, series = {Proceedings of Machine Learning Research}, address = {Ljubljana, Slovenia}, month = {05--06 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v8/re10a/re10a.pdf}, url = {https://proceedings.mlr.press/v8/re10a.html}, abstract = {Several works showed that biomolecular data integration is a key issue to improve the prediction of gene functions. Quite surprisingly only little attention has been devoted to data integration for gene function prediction through ensemble methods. In this work we show that relatively simple ensemble methods are competitive and in some cases are also able to outperform state-of-the-art data integration techniques for gene function prediction.} }
Endnote
%0 Conference Paper %T Simple ensemble methods are competitive with state-of-the-art data integration methods for gene function prediction %A Matteo Ré %A Giorgio Valentini %B Proceedings of the third International Workshop on Machine Learning in Systems Biology %C Proceedings of Machine Learning Research %D 2009 %E Sašo Džeroski %E Pierre Guerts %E Juho Rousu %F pmlr-v8-re10a %I PMLR %P 98--111 %U https://proceedings.mlr.press/v8/re10a.html %V 8 %X Several works showed that biomolecular data integration is a key issue to improve the prediction of gene functions. Quite surprisingly only little attention has been devoted to data integration for gene function prediction through ensemble methods. In this work we show that relatively simple ensemble methods are competitive and in some cases are also able to outperform state-of-the-art data integration techniques for gene function prediction.
RIS
TY - CPAPER TI - Simple ensemble methods are competitive with state-of-the-art data integration methods for gene function prediction AU - Matteo Ré AU - Giorgio Valentini BT - Proceedings of the third International Workshop on Machine Learning in Systems Biology DA - 2009/03/02 ED - Sašo Džeroski ED - Pierre Guerts ED - Juho Rousu ID - pmlr-v8-re10a PB - PMLR DP - Proceedings of Machine Learning Research VL - 8 SP - 98 EP - 111 L1 - http://proceedings.mlr.press/v8/re10a/re10a.pdf UR - https://proceedings.mlr.press/v8/re10a.html AB - Several works showed that biomolecular data integration is a key issue to improve the prediction of gene functions. Quite surprisingly only little attention has been devoted to data integration for gene function prediction through ensemble methods. In this work we show that relatively simple ensemble methods are competitive and in some cases are also able to outperform state-of-the-art data integration techniques for gene function prediction. ER -
APA
Ré, M. & Valentini, G.. (2009). Simple ensemble methods are competitive with state-of-the-art data integration methods for gene function prediction. Proceedings of the third International Workshop on Machine Learning in Systems Biology, in Proceedings of Machine Learning Research 8:98-111 Available from https://proceedings.mlr.press/v8/re10a.html.

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