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[PDF] Top 20 Novel approaches to biclustering and gene functional classification in microarray gene expression data

Has 10000 "Novel approaches to biclustering and gene functional classification in microarray gene expression data" found on our website. Below are the top 20 most common "Novel approaches to biclustering and gene functional classification in microarray gene expression data".

Novel approaches to biclustering and gene functional classification in microarray gene expression data

Novel approaches to biclustering and gene functional classification in microarray gene expression data

... Unsupervised classification is carried out when we have little information about the true classes present (few labels) or when we wish to discover new classes in a ...the gene expression context ... See full document

143

A Weighted Mutual Information Biclustering Algorithm for Gene Expression Data

A Weighted Mutual Information Biclustering Algorithm for Gene Expression Data

... dimensional data, whose a subset of genes are co-regulated under a subset of ...of gene expression data. In this paper, we present a novel biclustering algorithm, which called ... See full document

18

A Parallel Algorithm for Gene Expressing Data Biclustering

A Parallel Algorithm for Gene Expressing Data Biclustering

... DNA microarray experiments, a key step in the analysis of gene expression data is to discover groups of genes that share similar transcriptional ...behavior. Microarray techniques may ... See full document

7

Configurable pattern-based evolutionary biclustering of gene expression data

Configurable pattern-based evolutionary biclustering of gene expression data

... for biclustering of gene expression data named ...existing approaches: the use of an evaluation measure able to detect shifting and scaling patterns (VE t ), and the possibility of ... See full document

22

A Novel Approach to Missing Data Estimation Technique for Microarray Gene Expression Data and Dimensionality Reduction

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 ...different approaches to missing value estimation methods and their impact on the hierarchical ...missing ... See full document

11

DNA Microarray Data Analysis: A Novel Biclustering Algorithm Approach

DNA Microarray Data Analysis: A Novel Biclustering Algorithm Approach

... the data of the ...of data are not likely to be ...the gene expression matrix and the set of data becomes more ...basic biclustering approaches to deal with the coherent ... See full document

12

Biclustering of Gene Expression Data by Correlation-Based Scatter Search

Biclustering of Gene Expression Data by Correlation-Based Scatter Search

... of data generated by microarray technology is very useful to understand how the genetic information becomes functional gene ...products. Biclustering algorithms can determine a group of ... See full document

17

A Study of Cancer Microarray Gene Expression Profile: Objectives and Approaches

A Study of Cancer Microarray Gene Expression Profile: Objectives and Approaches

... The gene finding studies are very important in microarray study because it is aimed to reduce the dimensionality of microarray dataset by selecting the most informative ...Moreover, ... See full document

6

A novel biclustering algorithm of binary microarray data: BiBinCons and BiBinAlter

A novel biclustering algorithm of binary microarray data: BiBinCons and BiBinAlter

... real microarray datasets: The Yeast cell cycle dataset which has been described and then pretreated in ...the expression of 2884 genes in 17 terms ans the Human B-cell Lymphoma dataset which has been ... See full document

14

Microarray Gene Expression Data Classification using a Hybrid Algorithm: MRMRAGA

Microarray Gene Expression Data Classification using a Hybrid Algorithm: MRMRAGA

... the classification model therefore, it is necessary to reduce feature in order to get good performance using feature selection ...mentioned approaches have been classified depending on their used criterion ... See full document

8

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

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

... basic functional unit of all living ...A gene is a segment of DNA, which contains the formula for the chemical composition of one particular ...protein. Gene expression is the process of ... See full document

6

Detect Key Gene Information in Classification of Microarray Data

Detect Key Gene Information in Classification of Microarray Data

... tumor classification from morphologic to ...a classification problem in machine learning. Generally, microarray expression experi- ments allow the recording of expression levels of ... See full document

10

Modified Whale Optimization Algorithm For Feature Selection In Micro Array Cancer Dataset

Modified Whale Optimization Algorithm For Feature Selection In Micro Array Cancer Dataset

... using gene expression analysis utilizing micro array ...and functional expression levels of thousands of genes can be measured in ...parallel. Microarray dataset largely differs from ... See full document

8

Gene Selection for Tumor Classification Using Microarray Gene Expression Data

Gene Selection for Tumor Classification Using Microarray Gene Expression Data

... multiclass classification, especially for systems like SVMs, doesn’t present an easy ...existing approaches for model selection use the leave-one-out (loo) related estimators which are considered ... See full document

6

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

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

... only by BIGA, on the study examined the biclusters of BIGA that were not similar to any of the other biclusters; that is, the biclusters with maximum pair-wise similarity scores , 0.05. In bicluster 109 (the maximum PO = ... See full document

37

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

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

... handle the gene expression data. The main goal of the CC is to find biclusters based on the maximal acceptable score of the MSR (Mean Squared Residue) evaluation function. To achieve this goal, ... See full document

9

GENE EXPRESSION DATA ANALYSIS USING DATA MINING ALGORITHMS FOR COLON CANCER

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 ... See full document

7

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 biclustering algorithm has problems to find enriched biclusters but the simGIC measure clearly makes the search process more ...the biclustering algorithm for GDS3289 and GDS2415 datasets when used the ... See full document

19

Effective gene selection techniques for classification of gene expression data

Effective gene selection techniques for classification of gene expression data

... the microarray in Step 3 for the biochemical reaction to ...The microarray will then be put in a scanner or on a screen as in Step 4; the data is collected in the form of a digital picture of the ... See full document

36

Vector Quantization of Microarray Gene Expression Data

Vector Quantization of Microarray Gene Expression Data

... average expression, and which have been omitted in almost all research findings globally, shall remain the largest ...the gene regulation process at a later ... See full document

5

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