[PDF] Top 20 A temporal precedence based clustering method for gene expression microarray data
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A temporal precedence based clustering method for gene expression microarray data
... Granger causality test is not restricted to only linear models, and it can be readily extended to include non- linear terms in case we observe any non-linear behavior in the data. Some examples of non-linear ... See full document
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Fast Gene Ontology based clustering for microarray experiments
... hierarchical clustering in R to generate gene clusters based on the GO distance ...and expression data is shown in Figure 1. GO-based clustering for the genes is ... See full document
8
Classification of Cancer Gene Subtypes from Clustering of Gene Expression Data
... Typically, microarray gene expression data obscure imperative information which is necessary for the understanding of molecular biology processes that occurs in a specific organism with ... See full document
5
Vector Quantization of Microarray Gene Expression Data
... The data mining methods are used to find human-interpretable patterns that describe the data, for example, clustering, associations and ...any clustering or classification technique is to ... See full document
5
Mixture modeling of microarray gene expression data
... the clustering of microarray expression data, in particular, of tissue samples on a very large number of ...genetic data with skewness removed by Box-Cox transfor- mation [3], and then ... See full document
5
Improved robustness in time series analysis of gene expression data by polynomial model based clustering
... novel clustering method, polynomial clustering (PMC), for dealing with missing and noisy ...traditional clustering methods only generated good clusters when ap- plied to high quality ...the ... See full document
11
Meta-analysis of microarray data using a pathway-based approach identifies a 37-gene expression signature for systemic lupus erythematosus in human peripheral blood mononuclear cells
... of data sets, similar normalization methods, statistical tests, and parameters were used with all data ...all data sets except data set 4 were normalized using Robust Multi-array ...each ... See full document
10
Novel approaches to biclustering and gene functional classification in microarray gene expression data
... ore, gene ex pression datasets may contain as yet undiscovered classes of genes (functional modules) or samples (cell ...or clustering, which searches for class structure w ithout reference to class ... See full document
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A robust approach based on Weibull distribution for clustering gene expression data
... (SOM) clustering algorithms to the same gene sub- sets as the WDCM and obtain the gene clusters, respec- ...the gene clusters produced by WDCM to those produced by the other ...the gene ... See full document
9
Model based cluster analysis of microarray gene expression data
... observed gene-expression levels so that they are more likely to have a normal distribution, which will reduce the number of clusters found in a model-based ...of gene-expression levels ... See full document
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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 ... See full document
5
Research on the Gene Sequence Data Mining Model Based on Information Theory and Data Dimension Reduction Algorithm
... of gene sequence data ...undertake gene expression data mining task in formal concept analysis ...numerical data and show the equivalent features of the two ...for gene ... See full document
5
Learning Gene Regulatory Network from Microarrays Based on Bayesian Network
... important gene in the disease of lung cancer. Thus, the method we propose can select representative genes from enormous microarray gene expression data and construct clean and ... See full document
7
Gene Selection for Tumor Classification Using Microarray Gene Expression Data
... of gene expression data ...using microarray data ...kernel based classifiers, genetic algorithms and Self- Organizing Maps (SOM) are widely applied for tumor classification [3, ... See full document
6
Cluster Rasch models for microarray gene expression data
... the microarray 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 ... See full document
13
Clustering Techniques Analysis for Microarray Data
... go. Clustering analysis is one of the statistical techniques that play an important role for elucidating the hidden patterns in gene expression ...How clustering can be useful for the ... See full document
6
Efficient Clustering for Gene Expression Data
... DNA microarray technology. In the process of mining gene expressions under multi-conditions microarray experiments, gene clustering is relatively a tough task, because of the features ... See full document
6
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, ... See full document
7
BioVLAB Microarray: Microarray Data Analysis in Virtual Environment
... in microarray data analysis is to execute multiple of analysis tasks as a single batch ...exploratory data analysis issue. To allow small biology labs to utilize gene expression ... See full document
7
Validation of hierarchical gene clusters using repeated measurements
... Hierarchical clustering is an unsupervised technique, which is a common approach to study protein and gene expression ...In clustering, the patterns of expression of different genes are ... See full document
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