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Genomic Comparison Using Data Mining Techniques Based on a Possibilistic Fuzzy Sets Model by Walter Balzano; Maria Rosaria Del Sorbo is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

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Current copiousness of genomic information stored in biological databases [Mar Albà, M., Lee, M., Pearl, D., Shepherd, F.M.G., Martin, A.J., Orengo, N., Kellam, C.A., 2001. P. VIDA: a virus database system for the organisation of virus genome open reading frames. Nuleic Acids Res. 133-136] makes ultimately feasible the proposal for an application of knowledge management aimed to discover general rules in subcellular phenomena. The goal of this work is primarily to discover relationships between genes by microarray analysis. The tools exploited come from clustering techniques and are mainly based on Knowledge Discovery in Databases (KDD) concepts [Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., 1996. From data mining to knowledge discovery in databases. AI Magazine 17(3), 37-54]. Starting from a data set, each element can be represented by a characteristic matrix, which sums up all data attributes. In this case data mining is oriented to perform a Pattern Recognition of related sequences, hidden in databases [Hand, D.J., Nicholas, A., 2005. Heard finding groups in gene expression data. J. Biomed. Biotechnol. 215-225]. Following a bottom up approach, the next refinement is to compare r

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Author

Walter Balzano; Maria Rosaria Del Sorbo

Publisher

Elsevier Science; Elsevier ; Elsevier BV (ISSN 0303-2647)

Published

2007

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)