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Genetic association analysis of complex diseases has been limited by heterogeneity in their clinical manifestations and genetic etiology. Research has made it possible to differentiate homogeneous subtypes of the disease phenotype. Currently, the most sophisticated subtyping methods perform unsupervised cluster analysis using only clinical features of a disorder, resulting in subtypes for which genetic...
Cluster analysis of gene expression data is one of the most useful tools for identifying biologically relevant groups of genes, however, gene expression data suffer severely from the problems of measurement noise, dimension curse, high redundancy between genes, and the functional annotation of genes is incomplete and imprecise. These properties lead to most of the traditional clustering algorithms...
Traditional Fuzzy c-means (FCM) algorithm is commonly used in unsupervised learning. However, there are some limitations. Cluster number should be determined and the cluster center should be initialized before classification. A new algorithm is proposed in the paper. The best cluster number is obtained by analyzing cluster validity function and the cluster center is initialized by HCM. The data set...
Cluster analysis is an important tool for discovering the structures and patterns hidden in gene expression data. In this paper, a new algorithm for clustering gene expression profiles is proposed. In this method, we find natural clusters in the data based on a competitive learning strategy. Using partially known modes as constraints in our method, we reduce the sensitivity of the clustering procedure...
Using a shape analysis technique, a dataset of 83 (20 wild-type and 63 mutated) HIV-1 protease crystal structures is analyzed for the shape changes in their binding pockets. The structures were reported with different bound inhibitors (ligands) and consist of a variety of mutations. Several geometrical and topological attributes based on the volumetric shape function and few other additional properties...
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