Expression data
Data Dependencies in the Quantitation of Affymetrix Gene Expression Data
... gene expression data is often made on the class of data rather than the data ...of data, it is assumed to work well on that entire class of data irrespective of the context in ...
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Managing and querying gene expression data using Curray
... ary expression data management systems including Bio- conductor/R because BioFlow and Curry are both SQL like text based query languages, built as front ends for MySQL, and BioFlow is capable of supporting ...
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On the selection of appropriate distances for gene expression data clustering
... We take into account 15 different distance measures. From this total, 6 are correlations, namely, Pearson (PE), Goodman-Kruskal (GK), Spearman (SP), Kendall (KE), Weighted Goodman-Kruskal (WGK) and, Rank-Magni- tude ...
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Application of simulated annealing to the biclustering of gene expression data
... Using SAB, we have shown that stochastic methods have the potential to give improved results for the bicluster search problem. SAB discovers more significant biclusters than Cheng and Church’s original node deletion ...
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Detecting microRNA activity from gene expression data
... gene expression data, such as the generation of clusters or gene lists, is ...microarray data set and cross reference/integrate it with miRNA prediction ...specific expression and 3’ UTR ...
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Challenges Analyzing RNA Seq Gene Expression Data
... quencing) data is very ...count data to a continuous variable or continue to work with count ...each data type, analysis tools have been developed and seem appropriate at first sight, but a deeper ...
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Linkage and association analyses of principal components in expression data
... 15 data set consists of 3554 expression levels from lymphoblastoid cell lines in 194 individuals from 14 three-generation Utah CEPH (Centre d'Etude du Polymorphisme Humain) ...the data providers ...
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Gene Expression Data Analysis for Stomach Cancer
... Gene expression data from public repositories (GEO and ArrayExpress) was compiled to apply a meta-analysis algorithm to compare expression of the genes across studies, identify differentially ...
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Consensus clustering and functional interpretation of gene expression data
... gene- expression clustering algorithm discordance using a direct measurement of similarity: the weighted-kappa ...gene-expression data using resampling techniques on a single clustering method has ...
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Cluster Rasch models for microarray gene expression data
... gene expression data are often measured with a great deal of noise, and that the sample size of tissues or cell lines, denoted by n, is usually very small compared to the number of genes in ...
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Vector algebra in the analysis of genome wide expression data
... genome-wide expression data involves numerous issues not discussed ...in expression levels is also an important issue and has been addressed by a number of different approaches, most often with a ...
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Computational Techniques to Recover Missing Gene Expression Data
... gene expression can be done by measuring the amount of mRNA ...gene expression measurements more efficiently by providing prediction techniques based on partial ...gene expression dataset by ...
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Determining Physical Mechanisms of Gene Expression Regulation from Single Cell Gene Expression Data
... gene expression distributions which can be used to estimate the kinetic parameters of gene expression bursting, namely the rate that genes turn on, the rate that genes turn off, and the rate of ...qPCR ...
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Silhouette Scores for Arbitrary Defined Groups in Gene Expression Data and Insights into Differential Expression Results
... identical expression patterns were collapsed. Expression data having those unique expression patterns were used for calculating distance defined as “1 – Spearman’s ...low expression ...
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A Novel Approach to Missing Data Estimation Technique for Microarray Gene Expression Data and Dimensionality Reduction
... integrated data sets used for the missing data ...missing data imputation method, in which the correlation structure between the gene and regression coefficients are used to estimate the missing ...
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GENE EXPRESSION DATA ANALYSIS USING DATA MINING ALGORITHMS FOR COLON CANCER
... of Data mining is used in various medical applications like tumor classification, protein structure prediction, gene classification, cancer classification based on microarray data, clustering of gene ...
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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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Computational Techniques To Recover Missing Data From Gene Expression Data
... gene expression data generally suffers from missing value problem due to a variety of experimental ...missing data points can adversely affect downstream analysis, many algorithms have been proposed ...
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Vector Quantization of Microarray Gene Expression Data
... gene expression data using the three variants of ...datasets, data log transformed, except in the case of Mus musculus dataset in which case data pre-processing was applied for zero filling ...
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A Survey on Data Mining Of Gene Expression Data for Gene Function Prediction
... ABSTRACT : Mining the gene expression data for predicting the gene functioning for the possibility of cancerous behavior and utilizing the same in prompt and precise diagnosis. This paper presents detail ...
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