[PDF] Top 20 A novel biclustering approach with iterative optimization to analyze gene expression data
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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
37
DNA Microarray Data Analysis: A Novel Biclustering Algorithm Approach
... microarray data analysis, collaborative filtering, market research, information retrieval, text mining, electoral trends, exchange analysis, and so ...experimental data for example, the goal of ... See full document
12
A Hybrid Nelder-Mead Method For Biclustering Of Gene Expression Data
... Levy approach explores the search space better than Nelder-Mead method and Tabu Search with ...the optimization techniques NM, NM with Levy and Tabu search with NM used in this work to discover coherent ... See full document
6
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
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 ... See full document
5
Greedy Two Way K-Means Clustering For Optimal Coherent Triclsuter
... to analyze the gene expression data it not only gives converge quickly but it provides the global solution (Feng Liu, ...the biclustering problem and it provides high accuracy (Baiyi ... See full document
6
Biclustering of Gene Expression Data by Correlation-Based Scatter Search
... the data set by using binary values and it is recursively applied until a submatrix with only one value is ...of biclustering algorithms based on metaheuristics such as evolution- ary approaches [20,21], ... See full document
17
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 towards meaningful ... See full document
22
A novel biclustering algorithm of binary microarray data: BiBinCons and BiBinAlter
... microarray data is still a problem that requires a continuous ...microarray data is one of the most important open ...two biclustering algorithms of binary microarray data, adopting the ... See full document
14
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
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 ...popular approach in this ...model gene functional modules, which may share ... See full document
143
Review on Emerging Pattern Analysis using Gene Sequences
... of expression levels for a huge number of genes, perhaps all genes of an organism, inside various diverse experimental samples It is particularly imperative to extract biologically important data from this ... See full document
5
Discovering transnosological molecular basis of human brain diseases using biclustering analysis of integrated gene expression data
... disease gene sets compared to the single disease-specific gene ...assigned gene sets by dividing the number of identified gene sets in each functional category by the total number of ... See full document
8
A Novel Approach to Missing Data Estimation Technique for Microarray Gene Expression Data and Dimensionality Reduction
... the gene expression ...integrated data sets used for the missing data ...missing data imputation method, in which the correlation structure between the gene and regression ... See full document
11
Biclustering for Microarray Data: A Short and Comprehensive Tutorial
... Some biclustering algorithms address the problem of finding coherent evolutions across the rows and/or columns of the data matrix regardless of their exact ...of gene expression data, ... See full document
5
A novel iterative approach for mapping local singularities from geochemical data
... of singularities f (α) are thermodynamic functions, i.e. sta- tistical averages that provide only macroscopic information about the scaling properties of fractals. Recently, some lo- calized approaches to the ... See full document
8
A Novel Approach to Analyze the Sentiment with Conjunctive Words
... (Kaur et.al, 2014) has defined the meaning of sentiment Analysis is to recognize and characterize the conclusions/feelings/estimations in composed content. They discussed the various approaches used to accomplish the ... See full document
5
Cancer Detection using Frequency Pattern Ant Colony Optimization
... based gene selection method to find out the critical genes for cancer classification when using microarray ...each gene is viewed as a town (node) on the TSP ...between gene pair (i, j) at time t. ... See full document
6
A novel approach to data mining using simplified swarm optimization
... Support Vector Machines (SVMs) are a group of supervised learning methods that can be applied to classification or regression. Theoretically, SVM is a well-motivated algorithm that is based on the statistical learning ... See full document
54
Prognostic Significance of EIF4G1 in Patients with Pancreatic Ductal Adenocarcinoma
... The RNA-seq and microarray data and clinical data of PDAC were downloaded from TCGA [19, 20], GSE21501 [21], and GSE28735 [22] in Mar 2018. Patients lacking clinical information were excluded. We identified ... See full document
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