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.
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Guthke, R., Schmidt-Heck, W., Hahn, D., Pfaff, M. (2002). Gene Expression Data Mining for Functional Genomics using Fuzzy Technology. In: Zimmermann, HJ., Tselentis, G., van Someren, M., Dounias, G. (eds) Advances in Computational Intelligence and Learning. International Series in Intelligent Technologies, vol 18. Springer, Dordrecht. https://doi.org/10.1007/978-94-010-0324-7_33
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DOI: https://doi.org/10.1007/978-94-010-0324-7_33
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