Junjun Ouyang

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Permission is granted to quote short excerpts and to reproduce figures and tables from this report, provided that the source of such material is fully acknowledged. ABSTRACT Analyzing large data sets requires proper understanding of the data in advance. This would help domain experts to influence the data mining process and to properly evaluate the results(More)
Various data mining techniques combined with sequence motif information in the promoter region of genes were applied to discover functional genes that are involved in the defense mechanism of systemic acquired resistance (SAR) in Arabidopsis thaliana. A series of K-Means clustering with difference-in-shape as distance measure was initially applied. A(More)
Permission is granted to quote short excerpts and to reproduce figures and tables from this report, provided that the source of such material is fully acknowledged. Abstract: The purpose of this study was to develop a method for identifying useful patterns in gene expression time-series data. We have developed a novel data mining approach that identifies(More)
Permission is granted to quote short excerpts and to reproduce figures and tables from this report, provided that the source of such material is fully acknowledged. Identification of co-expressed genes sharing similar biological behaviors is an essential step in functional genomics. Traditional clustering techniques are generally based on overall similarity(More)
Permission is granted to quote short excerpts and to reproduce figures and tables from this report, provided that the source of such material is fully acknowledged. Abstract. For the purpose of gene identification, we propose an approach to gene expression data mining that uses a combination of unsupervised and supervised learning techniques to search for(More)
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