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Supervised Learning in the Gene Ontology Part II: A Bottom-Up Algorithm

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3700)

Abstract

Prediction of gene function for expression profiles introduces a new problem for supervised learning algorithms. The decision classes are taken from an ontology, which defines relationships between the classes. Supervised algorithms, on the other hand, assumes that the classes are unrelated. Hence, we introduce a new algorithm which can take these relationships into account. This is tested on a microarray data set created from human fibroblast cells and on several artificial data sets. Since standard performance measures do not apply to this problem, we also introduce several new measures for measuring classification performance in an ontology.

Keywords

Gene Ontology Irrelevant Attribute Decision Class Training Accuracy Conditional Attribute 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  1. 1.Department of BiologyNorwegian University of Science and TechnologyTrondheimNorway

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