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- W2318038487 abstract "Micro array technology is one of the important biotechnological means that allows recording the expression levels of thousands of genes simultaneously within a number of different samples. An important application of micro array gene expression data is to classify samples according to their gene expression profiles. The gene expression dataset can be represented by an expression table, where each row corresponds to one particular gene, each column to a sample. The relevance of each attribute (attribute represents the gene expression conversion into numerical values) with respect to the class label and the redundancy between two attributes in terms of mutual information are calculated using supervised similarity measure. The proposed system uses supervised attribute clustering algorithm which determines the relevance of each attribute and growing the cluster around each relevant attribute by adding one attribute after the other. Min-hash algorithm is used to reduce the redundancy between the genes and also reduce the cluster size. The performance of the system can be improved by reducing the redundancy of genes." @default.
- W2318038487 created "2016-06-24" @default.
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- W2318038487 date "2013-01-01" @default.
- W2318038487 modified "2023-09-25" @default.
- W2318038487 title "Gene Selection for Sample Classification in Microarray: Clustering Based Method" @default.
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- W2318038487 doi "https://doi.org/10.9790/0661-01031619" @default.
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