The abundance of genomic data now available in biomedical research has stimulated the development of sophisticated statistical methods for interpreting the data, and of special visualization tools for displaying the r...
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DNA microarrays enable the detection of genetic changes attributable to cancer by simultaneously analyzing the expression of thousands of genes. However, the identification of most relevant genes from thousands of gen...
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ISBN:
(纸本)9781538682166
DNA microarrays enable the detection of genetic changes attributable to cancer by simultaneously analyzing the expression of thousands of genes. However, the identification of most relevant genes from thousands of gene expressions available in each biological sample, for cancer classification pose a great challenge. Although researchers have applied BPSO based wrapper approaches to get most relevant genes prior to cancer classification, these approaches didn't achieve good classification accuracy due to the premature convergence caused by local stagnation problem. This paper proposes an improved Binary Particle Swarm Optimization (iBPSO) to tackle these issues. The proposed iBPSO based wrapper is examined using Naive-Bayes (NB), k-Nearest Neighbor (kNN), and Support Vector Machines (SVM) classifiers with stratified 5-fold cross-validation. The proposed iBPSO exhibited its efficacy in terms of classification accuracy and the number of selected genes in comparison to standard BPSO on six benchmark cancer microarraydatasets. Our proposed iBPSO also effectively escapes from local minima stagnation.
After providing a brief introduction to microarray chips and experimental details, this overview discusses analysis techniques. dataanalysis from microarray experiments generally involves two parts: acquiring and nor...
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After providing a brief introduction to microarray chips and experimental details, this overview discusses analysis techniques. dataanalysis from microarray experiments generally involves two parts: acquiring and normalizing the data, and interpreting it. This unit focuses mostly on the latter, as it is less technology-specific.
A Bayesian network expansion algorithm called BN+1 was developed to identify undocumented gene interactions in a known pathway using microarray gene expression data. In our recent paper, the BN+1 algorithm has been su...
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A Bayesian network expansion algorithm called BN+1 was developed to identify undocumented gene interactions in a known pathway using microarray gene expression data. In our recent paper, the BN+1 algorithm has been successfully used to identify key regulators including uspE in the E. coli ROS pathway and biofilm formation.18 In this report, a synthetic network was designed to further evaluate this algorithm. The BN+1 method was found to identify both linear and nonlinear relationships and correctly identify variables near the starting network. Using experimentally derived data, the BN+1 method identifies the gene fdhE as a potentially new ROS regulator. Finally, a range of possible score cutoff methods are explored to identify a set of criteria for selecting BN+1 calls.
After providing a brief introduction to microarray chips and experimental details, this overview discusses analysis techniques. dataanalysis from microarray experiments generally involves two parts: acquiring and nor...
详细信息
After providing a brief introduction to microarray chips and experimental details, this overview discusses analysis techniques. dataanalysis from microarray experiments generally involves two parts: acquiring and normalizing the data, and interpreting it. This unit focuses mostly on the latter, as it is less technology-specific.
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