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Gene Expression Data Mining for Functional Genomics using Fuzzy Technology

  • Reinhard Guthke
  • Wolfgang Schmidt-Heck
  • Daniel Hahn
  • Michael Pfaff
Part of the International Series in Intelligent Technologies book series (ISIT, volume 18)

Abstract

Methods for supervised and unsupervised clustering and machine learning were studied in order to automatically model relationships between gene expression data and gene functions of the microorganism Escherichia coli. From a pre-selected subset of 265 genes (belonging to 3 functional groups) the function has been predicted with an accuracy of 63–71 % by various data mining methods described in this paper. Whereas some of these methods, i.e. K-means clustering, Kohonen’s self-organizing maps (SOM), Eisen’s hierarchical clustering and Quinlan’s C4.5 decision tree induction algorithm have been applied to gene expression data analysis in the literature already, the fuzzy approach for gene expression data analysis is introduced by the authors. The fuzzy-C-means algorithm (FCM) and the Gustafson-Kessel algorithm for unsupervised clustering as well as the Adaptive Neuro-Fuzzy Inference System (ANFIS) were successfully applied to the functional classification of E. coli genes.

Key words

fuzzy clustering machine leaming biotechnology dna chips microarrays escherichia coli 

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Copyright information

© Springer Science+Business Media New York 2002

Authors and Affiliations

  • Reinhard Guthke
    • 1
  • Wolfgang Schmidt-Heck
    • 1
  • Daniel Hahn
    • 1
  • Michael Pfaff
    • 2
  1. 1.Hans Knöll Institute for Natural Products ResearchJenaGermany
  2. 2.BioControl Jena GmbHJenaGermany

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