Relevant Gene Selection and Classification of Leukemia Gene Expression Data

2016 ◽  
pp. 503-510
Author(s):  
S. Jacophine Susmi ◽  
H. Khanna Nehemiah ◽  
A. Kannan ◽  
Jabez Christopher
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Selection ◽  
Expression Data ◽  
Relevant Gene

A model for gene selection and classification of gene expression data

2007 ◽  
Vol 11 (2) ◽  
pp. 219-222 ◽  
Author(s):  
Mohd Saberi Mohamad ◽  
Sigeru Omatu ◽  
Safaai Deris ◽  
Siti Zaiton Mohd Hashim
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Selection ◽  
Expression Data

Gene Selection Using Parallel Lion Optimization Method in Microarray Data for Cancer Classification

2019 ◽  
Vol 9 (6) ◽  
pp. 1294-1300 ◽  
Author(s):  
A. Sampathkumar ◽  
P. Vivekanandan
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Microarray Data ◽  
Gene Selection ◽  
Optimization Method ◽  
Vital Role ◽  
Expression Data ◽  
Complicated Process ◽  
Numerous Data ◽  
Relevant Gene

In the field of bioinformatics research, a large volume of genetic data has been generated. Availability of higher throughput devices at lower cost has contributed to this generation of huge volumetric data. Handling such numerous data has become extremely challenging for selecting the relevant disease-causing gene. The development of microarray technology provides higher chances of cancer diagnosis, by enabling to measure the expression level of multiple genes at the same stretch. Selecting the relevant gene by using classifiers for investigation of gene expression data is a complicated process. Proper identification of gene from the gene expression datasets plays a vital role in improving the accuracy of classification. In this article, identification of the highly relevant gene from the gene expression data for cancer treatment is discussed in detail. By using modified meta-heuristic approach, known as 'parallel lion optimization' (PLOA) for selecting genes from microarray data that can classify various cancer sub-types with more accuracy. The experimental results depict that PLOA outperforms than LOA and other well-known approaches, considering the five benchmark cancer gene expression dataset. It returns 99% classification accuracy for the dataset namely Prostate, Lung, Leukemia and Central Nervous system (CNS) for top 200 genes. Prostate and Lymphoma dataset PLOA is 99.19% and 99.93% respectively. On evaluating the result with other algorithm, the higher level of accuracy in gene selection is achieved by the proposed algorithm.

scholarly journals Hybrid Correlation based Gene Selection for Accurate Cancer Classification of Gene Expression Data

2012 ◽  
Vol 43 (14) ◽  
pp. 13-18 ◽  
Author(s):  
Vibhav PrakashSingh ◽  
Singh Gaurav Arvind ◽  
Arindam G Mahapatra
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Selection ◽  
Cancer Classification ◽  
Expression Data ◽  
Selection For
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