Volume: 7, Issue: 2(2009)
pp. 269-285 DOI: 10.1142/S0219720009004072
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Full Text (PDF, 2,650KB)
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| Title: |
SUPERVISED ENSEMBLES OF PREDICTION METHODS FOR SUBCELLULAR LOCALIZATION |
| Author(s): |
JOHANNES ASSFALG
Institute for Informatics, Ludwig-Maximilians-Universität München, Oettingenstrasse 67, 80538 Munich, GermanyJING GONG
Institute for Informatics, Ludwig-Maximilians-Universität München, Oettingenstrasse 67, 80538 Munich, GermanyHANS-PETER KRIEGEL
Institute for Informatics, Ludwig-Maximilians-Universität München, Oettingenstrasse 67, 80538 Munich, GermanyALEXEY PRYAKHIN
Institute for Informatics, Ludwig-Maximilians-Universität München, Oettingenstrasse 67, 80538 Munich, GermanyTIANDI WEI
Institute for Informatics, Ludwig-Maximilians-Universität München, Oettingenstrasse 67, 80538 Munich, GermanyARTHUR ZIMEK
Corresponding author.
Institute for Informatics, Ludwig-Maximilians-Universität München, Oettingenstrasse 67, 80538 Munich, Germany
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| History: |
Received 30 May 2008
Revised 15 October 2008
Accepted 18 October 2008
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| Abstract: |
In the past decade, many automated prediction methods for the subcellular localization of proteins have been proposed, utilizing a wide range of principles and learning approaches. Based on an experimental evaluation of different methods and their theoretical properties, we propose to combine a well-balanced set of existing approaches to new, ensemble-based prediction methods. The experimental evaluation shows that our ensembles improve substantially over the underlying base methods. |
| Keywords: |
Subcellular localization of proteins; ensemble classifier
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