Biography:

Dr. Aljawarneh received a BSc degree in Computer Sciences from Jordan Yarmouk University with distinction grade , and a MSc degree in Web Engineering and Design from the University of Western Sydney, Syndey, Australia, in 2003. After that, he taught a number of modules such as Web Technology, and Computer and Internet Ethics at the Jordan University of Science and Technology. He received his PhD from the department of Software Engineering, Northumbria University, UK. His research interests include web security, software engineering, and e-commerce. Currently, he is working at Isra Private University as assistant Prof. He has presented a number of conference papers and journals. As well as participated in a number of conference and IT days in Jordan and the UK. Finally, he has been granted a funding from King Abdellah Funding for development (KAFD) for performing web client authentication system in e-systems.

Abstract:

Classification of genes based on semantic web technologies and data mining

Today, it is possible to monitor a gene expression on a genomic scale using hierarchical clustering and k-means partitioning which are being the most popular methods. Several tools make use of the GO ontologies or the gene associations provided by consortium members or even individuals. While some progress has been made in addressing the gene expression data including classification and analysis of gene data, current methods are restricted by the limitations of the clustering and visualizations techniques. For example, Avadis, BiNGOb and DAVID tools are based on visualization for gene expression data. In visualization, gene annotations are visualized in as a table view and so the granularity of the GO DAG can be viewed freely by the user or use CLASSIFI (Cluster Assignment for Biological Inference) which is a data-mining tool that can be used to identify significant co-clustering of genes with similar functional properties such as cellular response to DNA damage. To enhance the bioinformatics, many researchers and technicians have preferred to match the clustering to the specifications of biomedical applications. In this paper, we have surveyed a number of clustering algorithms for different approaches and data types. A new tool which is based on semantic web technologies and data mining has been developed for gene classification. The architecture of the tool consists of the following components: RDFa annotation for elements of gene attributes, RDFa extractor, RDF parser, and data analyzer.