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Premalatha, K.
- A Survey on Feature Selection Methods in Microarray Gene Expression Data for Cancer Classification
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Authors
Affiliations
1 School of Information Technology and Engineering, VIT University, Vellore, IN
2 Department of CSE, Bannari Amman Institute of Technology, Sathyamangalam, IN
3 Department of ECE, K.S. Rangasamy College of Technology, Tiruchengode, IN
1 School of Information Technology and Engineering, VIT University, Vellore, IN
2 Department of CSE, Bannari Amman Institute of Technology, Sathyamangalam, IN
3 Department of ECE, K.S. Rangasamy College of Technology, Tiruchengode, IN
Source
Research Journal of Pharmacy and Technology, Vol 10, No 5 (2017), Pagination: 1395-1401Abstract
Microarray technology is commonly used in the study of disease diagnosis using gene expression levels. It not only received the attention of the research community but also has a wide range of applications. The success of microarray technology depends on the precision of measurement, the usage of tools in data mining, analytical methods and statistical modeling. The feature selection methods are used to find an informative representation, by removing noisy and irrelevant features which would improve the classification performance. There exist several works in the literature to select the significant features from the microarray. This paper reviews the feature selection methods used to select significant genes from the microarray gene expression data for cancer classification.Keywords
Microarray, Feature Selection, Gene Expression, Cancer Classification, Gene Selection.
- Particle Swarm Optimization for Triclustering High Dimensional Microarray Gene Expression Data
Abstract Views :169 |
PDF Views:0
Authors
Affiliations
1 Anna University, Chennai, IN
2 Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Sathyamangalam, IN
1 Anna University, Chennai, IN
2 Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Sathyamangalam, IN
Source
Research Journal of Pharmacy and Technology, Vol 12, No 5 (2019), Pagination: 2222-2228Abstract
The study of high dimensional microarray gene expression data represents the large computational challenge due to its huge volume of the data. Many clustering techniques are applied to extract the coexpressed genes over the samples. Biclustering improved the traditional clustering by grouping the genes that similarly expressed over only a subset of samples. However, to cluster the high dimensional data with three dimensions such as genes, samples and time points, Triclustering technique is employed for grouping the coexpressed genes over a subset of samples under a subset of time points which imposes huge computational burden. In this paper, Particle Swarm Optimization technique is applied to extract the triclusters from the high dimensional data with objective function as Mean Square Residue. The algorithm is applied to three real life microarray gene expression data and the performance of the work is analyzed using the objective function. The biological significances of the extracted triclusters from all the three datasets are also analyzed. The biological significance analysis are also compared with other triclustering algorithms and the proposed work outperforms the other algorithms.Keywords
Particle Swarm Optimization, Triclustering, High Dimensional Data, Microarray Gene Expression Data, Mean Square Residue.References
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