[PDF] Top 20 Biclustering of Gene Expression Data by Correlation-Based Scatter Search
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Biclustering of Gene Expression Data by Correlation-Based Scatter Search
... The comparison is not an easy task because the number of biclusters, their size or what kind of patterns are found are very different for each method. Table 2 presents the number of biclusters for each method, the ... See full document
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BiFree: An Efficient Biclustering Technique for Gene Expression Data Using Two Layer Free Weighted Bipartite Graph Crossing Minimization
... for gene expression data provides a global view of the ...of gene expression data with simultaneous grouping of genes and ...Several biclustering techniques have been ... See full document
8
A Weighted Mutual Information Biclustering Algorithm for Gene Expression Data
... of biclustering algorithms use ordinary Euclidean distance as the sim- ilarity measurement between genes, but Euclidean distance can only detect certain linear relationship of gene expression ... See full document
18
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 ...model gene functional modules, which may share gene ...large gene ... See full document
143
Configurable pattern-based evolutionary biclustering of gene expression data
... existing biclustering approaches base their search for biclusters on evaluation ...of biclustering tools that follow different strate- gies and algorithmic concepts which guide the search ... See full document
22
A Hybrid Nelder-Mead Method For Biclustering Of Gene Expression Data
... Therefore, biclustering algorithms have been preferred to standard clustering techniques to identify local patterns from gene expression data ...sets. Biclustering is a data ... See full document
6
An Extensive Survey On Biclustering Approaches And Algorithms For Gene Expression Data
... in expression level during several experimental conditions. Gene clusters are not properly recognized when the clustering methods use huge gene expression ...considers gene ... See full document
9
Biclustering of Gene Expression Data using a Two Phase Method
... of gene expression profiling techniques such as DNA microarray has made it possible to simultaneously analyze expression levels for thousands of genes under a number of different conditions ...[1]. ... See full document
5
Application of simulated annealing to the biclustering of gene expression data
... Gene expression datasets are continually growing in size as more experiments are carried out, and as experimental capac- ity ...the expression of genes to be highly similar under one set of ... See full document
7
A novel biclustering approach with iterative optimization to analyze gene expression data
... of biclustering algorithms has allowed biologists to start unraveling the underlying functional mechanisms in living ...alternative biclustering technique, since it was designed to address the conventional ... See full document
37
BiGGEsTS: integrated environment for biclustering analysis of time series gene expression data
... Biclustering groups genes and conditions based on rela- tions inferred from data, relying strictly on computational methods. Researchers are usually interested in analyzing the results looking for ... See full document
11
A comparison and evaluation of five biclustering algorithms by quantifying goodness of biclusters for gene expression data
... In our study, the results are generally consistent with several other surveys of biclus- tering algorithms. Like Prelic et al. [5] and Richards et al. [32], we find that ISA is an effective algorithm that can generate ... See full document
10
Discovering transnosological molecular basis of human brain diseases using biclustering analysis of integrated gene expression data
... integrative gene expression analysis, we combined three microarray datasets into a single dataset by adjusting batch-specific effects using ComBat method ...the biclustering method to the combined ... See full document
8
Optimization of Biclustering Algorithm Based on Greedy Randomized Adaptive Search Procedure
... The biclustering algorithm proposed by Hartigan [1] is clustering based on the rows and columns of gene expression matrices ...The biclustering algorithm is a new clustering method, it ... See full document
15
Biclustering for Microarray Data: A Short and Comprehensive Tutorial
... on biclustering for the analysis of gene expression data obtained from microarray ...to gene expression data are limited by the existence of a number of experimental ... See full document
5
Pairwise gene GO-based measures for biclustering of high-dimensional expression data
... raw data of the human datasets were generated in the context of clinical experiments with patients that suffer different cancer ...high-dimensional gene expression datasets from a biclustering ... See full document
19
A polynomial time biclustering algorithm for finding approximate expression patterns in gene expression time series
... series gene expression data, obtained from microar- ray experiments performed in successive instants of time, can be used to study a wide range of biological problems [1], and to unravel the ... See full document
39
A Parallel Algorithm for Gene Expressing Data Biclustering
... parallel biclustering algorithm P-bicluster for gene expression ...data. Based on the anti-monotones property of the quality of the data sets with their sizes, the algorithm uses ... See full document
7
Greedy Two Way K-Means Clustering For Optimal Coherent Triclsuter
... the data can be classified as three ways i) Grouping of data in one dimension is called as clustering ii) Grouping of data in two-dimension is called as biclustering iii) Grouping of ... See full document
6
Analysis of Population Based Metaheuristic Used for Gene Clustering
... of gene expression in cancer diagnosis, but it has a drawback that it does not obey some of the assumptions of parametric statistical methods, such as the assumption of normal distribution or independent ... See full document
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