Automatic generation of biclusters from gene expression data using multi-objective simulated annealing approach

2016 ◽  
Author(s):  
Pracheta Sahoo ◽  
Sudipta Acharya ◽  
Sriparna Saha
Keyword(s):  
Gene Expression ◽  
Simulated Annealing ◽  
Gene Expression Data ◽  
Automatic Generation ◽  
Expression Data ◽  
Multi Objective

scholarly journals Biclustering Gene Expression Data Using Genetic Simulated Annealing Algorithm

2019 ◽  
Vol 8 (6) ◽  
pp. 3717-3720
Keyword(s):  
Gene Expression ◽  
Simulated Annealing ◽  
Gene Expression Data ◽  
Missing Values ◽  
Simulated Annealing Algorithm ◽  
Expression Data ◽  
Genetic Simulated Annealing Algorithm ◽  
Gene Expression Matrix ◽  
The Matrix ◽  
Missing Items

DNA microarray technology produces gene expression matrix that consists of an inexorably missing entries due to poor experimental procedures. The missing values are predicted in the matrix for gene expression data are considered to be essential, since most algorithms analyse the gene expression that usually needs a matrix without missing values. In order to address this issue, the present study biclustering Genetic based Simulated Annealing (Genetic SA) algorithm to predict the items that are missing in the gene expression data. The present study uses biclustering method that is considered to be essential for clustering the gene expression data. The performance evaluation shows that the proposed Genetic SA for gene data expression predicts the missing items in an accurate manner than the existing methods.

Analysis of Micro Array Gene Expression Data Using Multi-Objective Binary Particle Swarm Optimization with Fuzzy Weighted Clustering (MOBPSO-FWC) Technique

2020 ◽  
Vol 10 (5) ◽  
pp. 1049-1056
Author(s):  
M. Kalaiarasu ◽  
J. Anitha
Keyword(s):  
Gene Expression ◽  
Particle Swarm Optimization ◽  
Gene Expression Data ◽  
Expression Profiles ◽  
Particle Swarm ◽  
Binary Particle Swarm Optimization ◽  
Expression Data ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Weighted Clustering

In the rapidly advancing field of genomics, microarray technologies have turned into a ground-breaking system on simultaneous monitoring the expression patterns of multiple genes under various arrangements of constraints. A fundamental errand is to propose diagnostic techniques to distinguish cluster of genes comparative expression designs and are initiated by comparative conditions. And furthermore, the relating investigation has issue is to cluster multi-condition gene expression data. To overcome these issues, the vast measure of data obtained by this technology, resort to clustering methods that distinguish clusters of genes of share similar expression profiles. The motivation of this work is to introduce a clustering method in microarray gene expression data analysis. Multi-Objective Binary Particle Swarm Optimization with Fuzzy Weighted Clustering (MOBPSOFWC) algorithm is proposed to analyze gene expression data. In high dimensionality, a quick heuristic based pre-processing technique is employed to diminish some of the basic domain features from the initial feature set. Since these pre-processed and reduced features are still high dimensional, the proposed MOBPSO algorithm is implemented in MATLAB tool used for finding further feature subsets. The investigative are directed to distinguish the execution of the proposed work with existing clustering approaches.

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