Fuzzy set-based microarray data analysis techniques for interesting block identification

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.

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Mining of Subspace Contrasting Sample Groups in Microarray Data

Lee¹,

Lee²

2011

Journal of Korean Institute of Intelligent Systems

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In this paper, we introduce the subspace contrasting group identification problem and propose an algorithm to solve the problem. In order to identify contrasting groups, the algorithm first determines two groups of which attribute values are in one of the contrasting ranges specified by the analyst, and searches for the contrasting groups while increasing the dimension of subspaces with an association rule mining strategy. Because the dimension of microarray data is likely to be tens of thousands, it is burdensome to find all contrasting groups over all possible subspaces by query generation. It is very useful in the sense that the proposed method allows to find those contrasting groups without analyst's involvement.

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Fuzzy set-based microarray data analysis techniques for interesting block identification

Cited by 4 publications

References 2 publications

Clustering microarray gene expression data using type 2 fuzzy logic

Clustering microarray gene expression data using type 2 fuzzy logic

Classification of Microarray Gene Expression Data by Gene Combinations using Fuzzy Logic MGC-FL

Mining of Subspace Contrasting Sample Groups in Microarray Data

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