%0 Journal Article
%T Biological Significance of Gene Expression Data using Similarity based Biclustering Algorithm
%A Bagyamani J
%A Thangavel
%A Rathipriya R
%J International Journal of Biometric and Bioinformatics
%D 2011
%I Computer Science Journals
%X Unlocking the complexity of a living organism¡¯s biological processes, functionsand genetic network is vital in learning how to improve the health of humankind.Genetic analysis, especially biclustering, is a significant step in this process.Though many biclustering methods exist, only few provide a query basedapproach for biologists to search the biclusters which contain a certain gene ofinterest. This proposed query based biclustering algorithm SIMBIC+ firstidentifies a functionally rich query gene. After identifying the query gene, sets ofgenes including query gene that show coherent expression patterns acrosssubsets of experimental conditions is identified. It performs simultaneousclustering on both row and column dimension to extract biclusters using Topdown approach. Since it uses novel ¡®ratio¡¯ based similarity measure, biclusterswith more coherence and with more biological meaning are identified. SIMBIC+uses score based approach with an aim of maximizing the similarity of thebicluster. Contribution entropy based condition selection and multiple row /column deletion methods are used to reduce the complexity of the algorithm toidentify biclusters with maximum similarity value. Experiments are conducted onYeast Saccharomyces dataset and the biclusters obtained are compared withbiclusters of popular MSB (Maximum Similarity Bicluster) algorithm. Thebiological significance of the biclusters obtained by the proposed algorithm andMSB are compared and the comparison proves that SIMBIC+ identifies biclusterswith more significant GO (Gene Ontology).
%K Data Mining
%K Bioinformatics
%K Biclustering
%K Gene Expression Data
%K Gene Selection
%K Top-Down
%U http://www.cscjournals.org/csc/manuscript/Journals/IJBB/volume4/Issue6/IJBB-80.pdf