الفهرس يوجد فقط 14 صفحة متاحة للعرض العام
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المستخلص
Recent advances in biotechnology allow researchers to measure expression levels for thou¬sands of genes simultaneously, across different conditions and over time. Analysis of data produced by such experiments offers potential insight into gene function and regulatory mechanisms. A key step in the analysis of gene expression data is the detection of groups of genes that manifest similar expression patterns. The corresponding algorithmic problem is to cluster multicondition gene expression patterns.
This thesis introduces a new clustering algorithm for gene expression data. The design of this proposed algorithm avoids the drawbacks and the disadvantages of the existing al¬gorithms of clustering gene expression data. The proposed aCORR clustering algorithm is tested and verified on real biological data sets. The thesis also presents an extension for the proposed aCORR algorithm to the scope of biclustering, resulting a new semi-jUzzy algo¬rithm named BISOFT. The development of the biclustering algorithm, BISOFT, is done by merging the ideas from the proposed custering algorithms, in addition to introducing new ideas for discovering hidden biclusters in the underlying data. The proposed biclustering al¬gorithm is also thoroughly tested and verified on classical data sets from different biological sources.