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[PDF] Top 20 A Parallel Algorithm for Gene Expressing Data Biclustering

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A Parallel Algorithm for Gene Expressing Data Biclustering

A Parallel Algorithm for Gene Expressing Data Biclustering

... on gene sequences, but also consider the variety in experimental ...Therefore, biclustering of gene expression data is to identify groups of genes that show a “similar” expression level or ... See full document

7

Biclustering of Gene Expression Data using a Two   Phase Method

Biclustering of Gene Expression Data using a Two Phase Method

... useful data mining technique which identifies coherent patterns from microarray gene expression ...a gene expression dataset is a subset of genes which exhibit similar expression patterns along a ... See full document

5

A novel biclustering approach with iterative optimization to analyze gene expression data

A novel biclustering approach with iterative optimization to analyze gene expression data

... best gene cover- age (highest) and overlap ...higher gene coverage due to the small number of clusters (Table ...adopt gene co-expression in the target ... See full document

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A Hybrid Nelder-Mead Method For Biclustering Of Gene Expression Data

A Hybrid Nelder-Mead Method For Biclustering Of Gene Expression Data

... ABSTRACT: Biclustering algorithms are used to identify local patterns from gene expression data sets and used to extract biologically relevant ...from gene expression data with minimum ... See full document

6

An Extensive Survey On Biclustering Approaches And Algorithms For Gene Expression Data

An Extensive Survey On Biclustering Approaches And Algorithms For Gene Expression Data

... then the block clustering finds the row (gene) or column (condition) iteratively, that time the largest reduction occurs within the block. After the block reduction, the block will be divided into two blocks by ... See full document

9

Biclustering for Microarray Data: A Short and Comprehensive Tutorial

Biclustering for Microarray Data: A Short and Comprehensive Tutorial

... each gene to a single ...the gene expression matrix have been proposed to ...as biclustering, which seeks to find sub-matrices, that is subgroups of genes and subgroups of columns, where the genes ... See full document

5

Biclustering of Gene Expression Data by Correlation-Based Scatter Search

Biclustering of Gene Expression Data by Correlation-Based Scatter Search

... from gene expression data. In this algorithm the proposed fitness function is based on the linear correlation among genes to detect shifting and scaling patterns from genes and an improvement method ... See full document

17

Construction of gene regulatory networks using biclustering and bayesian networks

Construction of gene regulatory networks using biclustering and bayesian networks

... of gene regulatory networks (GRNs) in ...wide data have been developed to unravel the complexity of gene ...scriptomic data measured by genome-wide DNA microarrays are traditionally used for ... See full document

20

A comparison and evaluation of five biclustering algorithms by quantifying goodness of biclusters for gene expression data

A comparison and evaluation of five biclustering algorithms by quantifying goodness of biclusters for gene expression data

... effective algorithm that can generate biclusters with high GO WE scores and PPI scores for large dataset ...ISA algorithm returned no bicluster, which was attributed to the fact that this dataset contains ... See full document

10

BiFree: An Efficient Biclustering Technique for 
                      Gene Expression Data Using Two Layer Free
                      Weighted Bipartite Graph Crossing Minimization

BiFree: An Efficient Biclustering Technique for Gene Expression Data Using Two Layer Free Weighted Bipartite Graph Crossing Minimization

... Most of the crossing minimization takes place in the first few iterations and hence we employ the weighted barycenter heuristic on the upper layer for only one iteration to place similar vertices in the vicinity of each ... See full document

8

Classifying human promoters by occupancy patterns identifies recurring sequence elements, combinatorial binding, and spatial interactions

Classifying human promoters by occupancy patterns identifies recurring sequence elements, combinatorial binding, and spatial interactions

... Downloaded data from ENCODE (GM12878/K562). TableS2 : Details of downloaded data control ...to biclustering algorithm. FigS2: Biclustering result of active TSS in K562 ...FigS3: ... See full document

19

BiGGEsTS: integrated environment for biclustering analysis of time series gene expression data

BiGGEsTS: integrated environment for biclustering analysis of time series gene expression data

... (BiclusterinG Gene Expression Time Series) is a free and open source graphical application using state-of- the-art biclustering algorithms specifically developed for analyzing gene expression ... See full document

11

Configurable pattern-based evolutionary biclustering of gene expression data

Configurable pattern-based evolutionary biclustering of gene expression data

... the data into clusters of samples and to iden- tify the differences between the genes that characterize such ...to gene expression data has also been broadly studied in the literature ...each ... See full document

22

Pairwise gene GO-based measures for biclustering of high-dimensional expression data

Pairwise gene GO-based measures for biclustering of high-dimensional expression data

... the algorithm performance is improved when the biological information is ...the algorithm to biclusters composed of groups of genes functionally ...the algorithm performance for high-dimensional ... See full document

19

A biclustering algorithm based on a Bicluster Enumeration Tree: application to DNA microarray data

A biclustering algorithm based on a Bicluster Enumeration Tree: application to DNA microarray data

... BiMine algorithm (Figure 2 (Algorithm 1)) uses a first function to built an initial tree (Init_BET) which is recur- sively extended by a second function ...from data matrix M with one gene and ... See full document

16

Greedy Two Way K-Means Clustering For Optimal Coherent Triclsuter

Greedy Two Way K-Means Clustering For Optimal Coherent Triclsuter

... optimal algorithm and make the probability for global convergence bigger (Wei Shen, ...the gene expression data it not only gives converge quickly but it provides the global solution (Feng Liu, ... See full document

6

A Study And Analysis Of Biclustering Algorithms To Identify Ppi Network And Significant Enrichments Of Genes In Biclusters Based On Gene Ontology And Kegg Pathway Database

A Study And Analysis Of Biclustering Algorithms To Identify Ppi Network And Significant Enrichments Of Genes In Biclusters Based On Gene Ontology And Kegg Pathway Database

... The gene randomization method used for small amount of ...the gene set as significant one for only few differentially expressed ...PPI data has been produced the results, which are used for ... See full document

6

A Weighted Mutual Information Biclustering Algorithm for Gene Expression Data

A Weighted Mutual Information Biclustering Algorithm for Gene Expression Data

... WMIB algorithm has similar fluctuation trend, which can show it’s good ...WMIB algorithm and IBWMSR algorithm exists many similarities, they both use fuzzy cluster to partition- ing the dataset, and ... See full document

18

Application of simulated annealing to the biclustering of gene expression data

Application of simulated annealing to the biclustering of gene expression data

... termed biclustering and was first introduced to gene expression analysis by Cheng and Church ...deletion algorithm in their search. The review of biclustering algorithms for biological ... See full document

7

A polynomial time biclustering algorithm for finding approximate expression patterns in gene expression time series

A polynomial time biclustering algorithm for finding approximate expression patterns in gene expression time series

... Parts of this work have appeared previously in [32]. However, this manu- script describes algorithmic and complexity details not included in the con- ference version of the paper. We also present a detailed comparison ... See full document

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