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Multitask Learning in Computational Biology

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons86758

Widmer,  C
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84153

Rätsch,  G
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Zitation

Widmer, C., & Rätsch, G. (2012). Multitask Learning in Computational Biology. ICML 2011 Unsupervised and Transfer Learning Workshop, 207-216.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000E-FDE1-7
Zusammenfassung
Computational Biology provides a wide range of applications for Multitask Learning (MTL) methods. As the generation of labels often is very costly in the biomedical domain, combining data from different related problems or tasks is a promising strategy to reduce label cost. In this paper, we present two problems from sequence biology, where MTL was successfully applied. For this, we use regularization-based MTL methods, with a special focus on the case of a hierarchical relationship between tasks. Furthermore, we propose strategies to refine the measure of task relatedness, which is of central importance in MTL and finally give some practical guidelines, when MTL strategies are likely to pay off.