Abstract
The paper presents results of the research verifying whether gene clustering that takes under consideration both gene expression values and similarity of GO terms improves a quality of rule-based description of the gene groups. The obtained results show that application of the Conditional Robust Fuzzy C-Medoids algorithm enables to obtain gene groups similar to the groups determined by domain experts. However, the differences observed in clustering influences a description quality of the groups. The rules determined cover more genes retaining their statistical significance. The rules induction and post-processing method presented in the paper takes under consideration, among others, a hierarchy of GO terms and a compound measure that evaluates the generated rules. The approach presented is unique, it makes possible to limit a number of rules determined considerably and to obtain rules that reflect varied biological knowledge even if they cover the same genes.
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Gruca, A., Kozielski, M., Sikora, M. (2009). Fuzzy Clustering and Gene Ontology Based Decision Rules for Identification and Description of Gene Groups. In: Cyran, K.A., Kozielski, S., Peters, J.F., Stańczyk, U., Wakulicz-Deja, A. (eds) Man-Machine Interactions. Advances in Intelligent and Soft Computing, vol 59. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00563-3_14
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DOI: https://doi.org/10.1007/978-3-642-00563-3_14
Publisher Name: Springer, Berlin, Heidelberg
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