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    This was brought to you by:

    block this user Jose M Martinez-Otzeta Trusted member

    Research Fellow

    Fundacion Tekniker, 20600, Eibar, Spain

    Combining Bayesian Networks, k Nearest Neighbours algorithm and Attribute Selection for Gene Expression Data

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    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.

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    Title : Combining Bayesian Networks, k Nearest Neighbours algorithm and Attribute Selection for Gene Expression Data
    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.
    Subject : unspecified
    Area : Computer Science
    Language : English
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    Url : http://www.sc.ehu.es/ccwrobot/publications/papers/sierra04combining.pdf.gz
    Doi : 10.1.1.61.9184

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