Clustering of gene-expression data
Clustering Algorithms: Their Application to Gene Expression Data
... AbstrAct: Gene expression data hide vital information required to understand the biological process that takes place in a particular organism in relation to its ...in gene expression ...
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Classification of Cancer Gene Subtypes from Clustering of Gene Expression Data
... microarray gene expression data obscure imperative information which is necessary for the understanding of molecular biology processes that occurs in a specific organism with respect to its ...
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Gene Expression Data Clustering and Visualization based on a Binary Hierarchical Clustering Framework
... unsupervised clustering of gene expression data using the BHC ...the data. It involves using the K-means algorithm to split the data into two classes, and then verify whether the ...
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ROUGH SET BASED CLUSTERING OF GENE EXPRESSION DATA: A SURVEY
... the expression levels of thousands of genes during important biological processes and across collections of related ...of gene expression data makes it difficult to be ...of clustering ...
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Fuzzy clustering of time series gene expression data with cubic spline
... biological data has been extracted from microarrays. Analysis of these data on the molecular level is revolutionary in medicine be- cause they are highly ...methodologies. Clustering of gene ...
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Fuzzy clustering of time series gene expression data with cubic-spline
... biological data has been extracted from microarrays. Analysis of these data on the molecular level is revolutionary in medicine be- cause they are highly ...methodologies. Clustering of gene ...
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Bayesian hierarchical clustering for studying cancer gene expression data with unknown statistics
... hierarchical clustering of gene expression data revealing hierarchical structure present in the data; third, it infers the number of clusters automatically from the data; and ...
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Efficient Clustering for Gene Expression Data
... biological data such as DNA sequences and microarray data have been increased ...the data, explore relationships between genes, understanding severe diseases and development of drugs for patterns ...
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Clustering gene expression data with repeated measurements
... different expression pat- terns between different types of ...to expression data (for example ...‘typical’ gene-expression data, nearly every software vendor is compelled to ...
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Gene Expression Data Clustering Analysis: A Survey
... Although gene expression clustering has been done by applying k-means, hierarchical clustering and SOMs algorithms, the desired features for clustering include minimum user input, ...
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Fuzzy Clustering Models for Gene Expression Data Analysis
... the clustering more reliable, a new fuzzy clustering approach is proposed based on FCM by utilizing kernel distance to measure the genes similar- ...the clustering process. Experiments on synthetic ...
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Consensus clustering and functional interpretation of gene expression data
... the gene-expression data; therefore multiple analyses should be performed and com- pared ...different clustering algorithms rather than to re-sample over the same ...consensus ...
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Consensus clustering and functional interpretation of gene expression data
... the gene-expression data; therefore multiple analyses should be performed and com- pared ...different clustering algorithms rather than to re-sample over the same ...consensus ...
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Clustering analysis for gene expression data: a methodological review
... merging clustering is an idea in which without set- ting the number of clusters a priori, the algorithm will converge to a partitioning which reveals the true number of clusters and pro- vides fairly accurate ...
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Incorporating heterogeneous biological data sources in clustering gene expression data
... chip-chip data are converted into the same form of gene expression data with pear- son correlation as its similarity ...interaction data and chip-chip data, the combined ...
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Semi-supervised consensus clustering for gene expression data analysis
... for gene expression datasets is ...for clustering microarray data. A study on semi-supervised clustering shows that with small amounts of prior knowledge, search-based approach tends to ...
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Feature-based clustering of stomach cancer gene expression data
... RNAseq gene expression data obtained from TCGA, ENSG gene names were first converted to standard gene names using the BIOMART R ...the clustering algorithm, clustering was ...
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Functional clustering of time series gene expression data by Granger causality
... perform gene clustering through the identification of Granger causality between and within sets of time series gene expression ...functional clustering, wherein genes would be clustered ...
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A robust approach based on Weibull distribution for clustering gene expression data
... by clustering their corresponding distribution ...cancer gene expression data sets we used, and then visually demon- strated the clustering results obtained using the WDCM for the three ...
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An effective fuzzy kernel clustering analysis approach for gene expression data
... Fuzzy clustering is an important tool for analyzing microarray ...fuzzy clustering method to microarray gene expression data is the choice of parameters with cluster number and ...
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