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Supervised Learning in the Gene Ontology Part I: A Rough Set Framework

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

Abstract

Prediction of gene function introduces a new learning problem where the decision classes associated with the objects (i.e., genes) are organized in a directed acyclic graph (DAG). Rough set theory, on the other hand, assumes that the classes are unrelated cannot handle this problem properly. To this end, we introduce a new rough set framework. The traditional decision system is extended into DAG decision system which can represent the DAG. From this system we develop several new operators, which can determine the known and the potential objects of a class and show how these sets can be combined with the usual rough set approximations. The properties of these operators are also investigated.

Keywords

Gene Ontology Directed Acyclic Graph Decision Class Information Vector Gene Ontology Consortium 
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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