%0 Journal Article %1 jbagyamani2013comparison %A J.Bagyamani, K.Thangavel %A R.Rathipriya, %D 2013 %E ACEEE, %J ACEEE Int. J. on Information Technology %K Average Biclustering Biological Correlation Data Expression Gene Mining Ontology Significance Value %N 1 %T Comparison of Biological Significance of Biclusters of SIMBIC and SIMBIC+ Biclustering Models %U /brokenurl#doi.searchdl.org/01/IJIT/3/1/1015.pdf %V 3 %X Query driven Biclustering Model refers to the problem of extracting biclusters based on a query gene or query condition. The extracted biclusters consist of a set of genes and a subset of conditions that are similar to the query gene or query condition and it includes the query input also. Two approaches applied for biclustering problems are top- down and bottom-up, based on how they tackle the problems. Top-down techniques 3, 4 start with the entire gene expression matrix and iteratively partition it into smaller sub-matrices. On the other hand, bottom-up approach starts with a randomly chosen set of biclusters that are iteratively modified, usually enlarged, until no local improvement is possible. In this paper, the biological significance of biclusters extracted using two query driven models viz SIMBIC and SIMBIC+ are compared.This paper is organized as follows. Section 2 analyzes the popular MSB algorithm and section 3 introduces an improved version of MSB namely SIMBIC model and the enhanced model of SIMBIC namely SIMBIC+ is presented in section 4. The experimental analysis and the biological significance are illustrated in section 5.

@article{jbagyamani2013comparison, abstract = {Query driven Biclustering Model refers to the problem of extracting biclusters based on a query gene or query condition. The extracted biclusters consist of a set of genes and a subset of conditions that are similar to the query gene or query condition and it includes the query input also. Two approaches applied for biclustering problems are top- down and bottom-up, based on how they tackle the problems. Top-down techniques [3, 4] start with the entire gene expression matrix and iteratively partition it into smaller sub-matrices. On the other hand, bottom-up approach starts with a randomly chosen set of biclusters that are iteratively modified, usually enlarged, until no local improvement is possible. In this paper, the biological significance of biclusters extracted using two query driven models viz SIMBIC and SIMBIC+ are compared.This paper is organized as follows. Section 2 analyzes the popular MSB algorithm and section 3 introduces an improved version of MSB namely SIMBIC model and the enhanced model of SIMBIC namely SIMBIC+ is presented in section 4. The experimental analysis and the biological significance are illustrated in section 5. }, added-at = {2013-03-09T06:54:57.000+0100}, author = {J.Bagyamani, K.Thangavel and R.Rathipriya}, biburl = {https://www.bibsonomy.org/bibtex/23e1e1b32d79619d883a22bae6a69d379/ideseditor}, editor = {ACEEE}, interhash = {94a1e011df2527a85cb1f341a8274fae}, intrahash = {3e1e1b32d79619d883a22bae6a69d379}, journal = {ACEEE Int. J. on Information Technology}, keywords = {Average Biclustering Biological Correlation Data Expression Gene Mining Ontology Significance Value}, month = mar, number = 1, timestamp = {2013-03-09T06:54:57.000+0100}, title = {Comparison of Biological Significance of Biclusters of SIMBIC and SIMBIC+ Biclustering Models }, url = {/brokenurl#doi.searchdl.org/01/IJIT/3/1/1015.pdf}, volume = 3, year = 2013 }