Abstract
In the last years, there has been a large growth in gene expression profiling technologies, which are expected to provide insight into cancer related cellular processes. Machine Learning algorithms, which are extensively applied in many areas of the real world, are not still popular in the Bioinformatics community. We report on the successful application of the combination of two supervised Machine Learning methods, Bayesian Networks and k Nearest Neighbours algorithms, to cancer class prediction problems in three DNA microarray datasets of huge dimensionality (Colon, Leukemia and NCI-60). The essential gene selection process in microarray domains is performed by a sequential search engine and after used for the Bayesian Network model learning. Once the genes are selected for the Bayesian Network paradigm, we combine this paradigm with the well known K NN algorithm in order to improve the classification accuracy.
This work was supported the University of the Basque Country under UPV 140.226-EA186/96 grant and by the Gipuzkoako Foru Aldundi Txit Gorena under OF761/2003 grant .
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Sierra, B., Lazkano, E., Martínez-Otzeta, J.M., Astigarraga, A. (2004). Combining Bayesian Networks, k Nearest Neighbours Algorithm and Attribute Selection for Gene Expression Data Analysis. In: Webb, G.I., Yu, X. (eds) AI 2004: Advances in Artificial Intelligence. AI 2004. Lecture Notes in Computer Science(), vol 3339. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30549-1_8
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