Reference : Iterative multi-task sequence labeling for predicting structural properties of proteins
Scientific congresses and symposiums : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/2268/129254
Iterative multi-task sequence labeling for predicting structural properties of proteins
English
Maes, Francis mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Becker, Julien mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Wehenkel, Louis mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
2011
ESANN 2011
Yes
No
International
978-2-87419-044-5
19th European Symposium on Artificial Neural Networks
from 27-04-2011 to 29-04-2011
Université Catholique de Louvain
Bruges
Belgium
[en] Bioinformatics ; Multitask learning ; Structured prediction
[en] Developing computational tools for predicting protein structural information given their amino acid sequence is of primary importance in protein science. Problems, such as the prediction of secondary structures, of solvent accessibility, or of disordered regions, can be expressed as sequence labeling problems and could be solved independently by existing machine learning based sequence labeling approaches. But, since these problems are closely related, we propose to rather approach them jointly in a multi-task approach. To this end, we introduce a new generic framework for iterative multi-task sequence labeling. We apply this - conceptually simple but quite effective - strategy to jointly solve a set of five protein annotation tasks. Our empirical results with two protein datasets show that the proposed strategy significantly outperforms the single-task approaches.
GIGA-Bioinformatics
Fonds pour la formation à la Recherche dans l'Industrie et dans l'Agriculture (Communauté française de Belgique) - FRIA
Researchers
http://hdl.handle.net/2268/129254

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