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)


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.


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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  1. 1.
    Ali, K.M., Pazzani, M.J.: HYDRA: A noise-tolerant relational concept learning algorithm. In: Bajcsy, R. (ed.) Proceedings of the 13th International Joint Conference on Artificial Intelligence (IJCAI 1993), pp. 1064–1071 (1993)Google Scholar
  2. 2.
    Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. ACM Press, New York (1999)Google Scholar
  3. 3.
    Brown, M.P.S., Grundy, W.N., Lin, D., Cristianini, N., Sugnet, C.W., Furey, T.S., Ares Jr., M., Haussler, D.: Knowledge-based analysis of microarray gene expression data by using support vector machines. In: Proceedings of the National Academy of Sciences, USA, vol. 97(1), pp. 262–267 (2000)Google Scholar
  4. 4.
    The Gene Ontology Consortium. Gene Ontology: Tool for the unification of biology. Nature Genetics 25(1), 25–29 (2000)Google Scholar
  5. 5.
    Fürnkranz, J.: Separate-and-conquer rule learning. Artificial Intelligence Review 13(1), 3–54 (1999); Occurs also as Technical Report OEFAI-TR-96-25 (1999)Google Scholar
  6. 6.
    Grzymala-Busse, J.W.: Knowledge acquisition under uncertainty – a rough set approach. Journal of Intelligent and Robotic System 1, 3–16 (1988)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Grzymala-Busse, J.W.: LERS – A system for learning from examples based on rough sets. In: Intelligent decision support: Handbook of Applications and Advances of Rough Sets Theory, pp. 3–18. Kluwer Academic Publishers, Dordrecht (1992)Google Scholar
  8. 8.
    Hvidsten, T.R., Komorowski, J., Sandvik, A.K., Lægreid, A.: Predicting gene function from gene expressions and ontologies. In: Proceedings of the Pacific Symposium on Biocomputing 6 (PSB 2001), pp. 299–310. World Scientific Press, Singapore (2001)Google Scholar
  9. 9.
    Iyer, W.R., Eisen, M.B., Ross, D.T., Schuler, G., Moore, T., Lee, J.C.F., Trent, J.M., Staudt, L.M., Hudson Jr., J., Boguski, M.S., Lashkari, D., Shalon, D., Botstein, D., Brown, P.O.: The transcriptional program in the response of human fibroblasts to serum. Science 283, 83–87 (1999)CrossRefGoogle Scholar
  10. 10.
    Korfhage, R.R.: Information Storage and Retrieval. Wiley, Chichester (1997)Google Scholar
  11. 11.
    Lægreid, A., Hvidsten, T.R., Midelfart, H., Komorowski, J., Sandvik, A.K.: Predicting Gene Ontology biological process from temporal gene expression patterns. Genome Research 13(5), 965–979 (2003)CrossRefGoogle Scholar
  12. 12.
    Midelfart, H.: Knowledge Discovery from cDNA Microarrays and a priori Knowledge. PhD thesis, Department of Computer and Information Science, Norwegian University of Science and Technology (NTNU) (2003), ISBN 82-471-5617-2Google Scholar
  13. 13.
    Midelfart, H.: Supervised learning in the gene ontology—part II: A rough sets framework. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets IV. LNCS, vol. 3700, pp. 98–124. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  14. 14.
    Midelfart, H., Komorowski, J.: A rough set approach to learning in a directed acyclic graph. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds.) RSCTC 2002. LNCS (LNAI), vol. 2475, pp. 144–155. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  15. 15.
    Midelfart, H., Lægreid, A., Komorowski, J.: Classification of gene expression data in an ontology. In: Crespo, J.L., Maojo, V., Martin, F. (eds.) ISMDA 2001. LNCS, vol. 2199, pp. 186–194. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  16. 16.
    Shatkay, H., Edwards, S., Wilbur, W.J., Boguski, M.: Genes, themes and microarrays: Using information retrieval for large-scale gene analysis. In: Proceedings of the 8th International Conference on Intelligent Systems for Molecular Biology (ISMB 2000), pp. 317–328. AAAI Press, Menlo Park (2000)Google Scholar

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© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

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

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