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Multitask Learning in Computational Biology




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.

Author(s): Widmer, C. and Rätsch, G.
Journal: JMLR W\&CP. ICML 2011 Unsupervised and Transfer Learning Workshop
Volume: 27
Pages: 207--216
Year: 2012
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0

Links: PDF


  title = {Multitask Learning in Computational Biology},
  author = {Widmer, C. and R{\"a}tsch, G.},
  journal = {JMLR W\&CP. ICML 2011 Unsupervised and Transfer Learning Workshop},
  volume = {27},
  pages = {207--216},
  year = {2012}