A Rough Set Framework for Learning in a Directed Acyclic Graph

Prediction of gene function from expression profiles introduces a new learning problem where the decision classes associated with the objects (i.e., genes) are organized in a directed acyclic graph (DAG). Standard learning methods such a Rough Sets assume that these classes are unrelated, and cannot handle this problem properly. To this end, we introduce an extended rough set framework with several new operators. We show how these operators can be used in an new learning algorithm.

[1]  Ronald W. Davis,et al.  Quantitative Monitoring of Gene Expression Patterns with a Complementary DNA Microarray , 1995, Science.

[2]  Jan Komorowski,et al.  Predicting Gene Function from Gene Expressions and Ontologies , 2000, Pacific Symposium on Biocomputing.

[3]  D Haussler,et al.  Knowledge-based analysis of microarray gene expression data by using support vector machines. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[4]  Salvatore Greco,et al.  A New Rough Set Approach to Multicriteria and Multiattribute Classification , 1998, Rough Sets and Current Trends in Computing.

[5]  Jan Komorowski,et al.  Classification of Gene Expression Data in an Ontology , 2001, ISMDA.

[6]  Wojciech Ziarko,et al.  Variable Precision Rough Set Model , 1993, J. Comput. Syst. Sci..

[7]  Jerzy W. Grzymala-Busse,et al.  LERS-A System for Learning from Examples Based on Rough Sets , 1992, Intelligent Decision Support.

[8]  Z. Pawlak,et al.  Rough sets perspective on data and knowledge , 2002 .

[9]  Robert R. Sokal,et al.  A statistical method for evaluating systematic relationships , 1958 .

[10]  Z. Pawlak.  Rough Sets: Theoretical Aspects of Reasoning about Data , 1991 .

[11]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[12]  D. Botstein,et al.  Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.