%0 Journal Article %1 noauthororeditor %A V.Bhuvaneswari, %D 2012 %J International Journal of Computer Science, Engineering and Applications (IJCSEA) %K networks speech %N 04 %P 79-98 %R 10.5121/ijcsea.2012.2409 %T Classification of Microarray Gene Expression Data by Gene Combinations using Fuzzy Logic (MGC-FL) %U http://airccse.org/journal/ijcsea/papers/2412ijcsea09.pdf %V 02 %X Feature selection has attracted a huge amount of interest in both research and application communities of data mining. Among the large amount of genes presented in gene expression data, only a small fraction of them is effective for performing a certain diagnostic test. Hence, one of the major tasks with the gene expression data is to find groups of co regulated genes whose collective expression is strongly associated with the sample categories or response variables. A framework is proposed in this paper to find informative gene combinations and to classify gene combinations belonging to its relevant subtype by using fuzzy logic. The genes are ranked based on their statistical scores and highly informative genes are filtered. Such genes are fuzzified to identify 2-gene and 3-gene combinations and the intermediate value for each gene is calculated to select top gene combinations to further classify gene lymphoma subtypes by using fuzzy rules. Finally the accuracy of top gene combinations is compared with clustering results. The classification is done using the gene combinations and it is analyzed to predict the accuracy of the results. The work is implemented using java language.

@article{noauthororeditor, abstract = {Feature selection has attracted a huge amount of interest in both research and application communities of data mining. Among the large amount of genes presented in gene expression data, only a small fraction of them is effective for performing a certain diagnostic test. Hence, one of the major tasks with the gene expression data is to find groups of co regulated genes whose collective expression is strongly associated with the sample categories or response variables. A framework is proposed in this paper to find informative gene combinations and to classify gene combinations belonging to its relevant subtype by using fuzzy logic. The genes are ranked based on their statistical scores and highly informative genes are filtered. Such genes are fuzzified to identify 2-gene and 3-gene combinations and the intermediate value for each gene is calculated to select top gene combinations to further classify gene lymphoma subtypes by using fuzzy rules. Finally the accuracy of top gene combinations is compared with clustering results. The classification is done using the gene combinations and it is analyzed to predict the accuracy of the results. The work is implemented using java language. }, added-at = {2018-10-19T11:29:13.000+0200}, author = {V.Bhuvaneswari}, biburl = {https://www.bibsonomy.org/bibtex/24721855dab1b4b6718de76a279a50606/ijcsea}, doi = {10.5121/ijcsea.2012.2409}, interhash = {120cf3da880c9ac66eaaf14e5d2bd2d8}, intrahash = {4721855dab1b4b6718de76a279a50606}, issn = {2230-9616}, journal = {International Journal of Computer Science, Engineering and Applications (IJCSEA)}, keywords = {networks speech}, month = {August}, number = 04, pages = {79-98}, timestamp = {2018-10-19T11:29:13.000+0200}, title = {Classification of Microarray Gene Expression Data by Gene Combinations using Fuzzy Logic (MGC-FL)}, url = {http://airccse.org/journal/ijcsea/papers/2412ijcsea09.pdf}, volume = 02, year = 2012 }