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Microarray technology has been broadly used for monitoring the expression levels of thousands of genes simultaneously, providing the opportunities of identifying disease-related genes by finding differentially expressed genes in different conditions. However, a great challenge of analyzing microarray data is the significant noise brought by different experimental settings, laboratory procedures, genetic...
A typical microarray gene expression dataset is usually both extremely sparse and imbalanced. To select multiple highly informative gene subsets for cancer classification and diagnosis, a new fuzzy granular support vector machine-recursive feature elimination algorithm (FGSVM-RFE) is designed in this paper. As a hybrid algorithm of statistical learning, fuzzy clustering, and granular computing, the...
Extracting a subset of informative genes from microarray expression data is a critical data preparation step in cancer classification and other biological function analyses. Though many algorithms have been developed, the support vector machine-recursive feature elimination (SVM-RFE) algorithm is one of the best gene feature selection algorithms. It assumes that a smaller "filter-out" factor...
Selecting informative and discriminative genes from huge microarray gene expression data is an important and challenging bioinformatics research topic. This paper proposes a fuzzy-granular method for the gene selection task. Firstly, genes are grouped into different function granules with the fuzzy C-means algorithm (FCM). And then informative genes in each cluster are selected with the signal to...
Selecting the most possibly cancer-related genes from huge microarray gene expression data is an important bioinformatics research topic due to its significance to improve human's understandability of the inherent cancer-resulting mechanism. This is actually a feature selection problem. The huge number of genes makes it impossible to execute an exhaustive search. In this work, we propose a recursive...
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