Gene subset selection in microarray data using entropic filtering for cancer classification

In gene selection for cancer classification using microarray data, we define an eigenvalue-ratio statistic to measure a gene's contribution to the joint discriminability when this gene is included into a set of genes. Based on this eigenvalue-ratio statistic, we define a novel hypothesis testing for gene statistical redundancy and propose two gene selection methods. Simulation studies illustrate the agreement between statistical redundancy testing and gene selection methods. Real data examples show the proposed gene selection methods can select a compact gene subset which can not only be used to build high quality cancer classifiers but also show biological relevance.

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Gene Subset Selection for Leukemia Classification Using Microarray Data

Current Bioinformatics ◽

10.2174/1574893613666181031141717 ◽

2019 ◽

Vol 14 (4) ◽

pp. 353-358 ◽

Cited By ~ 3

Author(s):

Mohamed Nisper Fathima Fajila

Keyword(s):

Evolutionary Algorithm ◽

Microarray Data ◽

Gene Selection ◽

Subset Selection ◽

Research Field ◽

Small Subset ◽

Gene Subset ◽

Highly Active ◽

Selection Gene ◽

Active Research

Background: Cancer subtype identification is an active research field which helps in the diagnosis of various cancers with proper treatments. Leukemia is one such cancer with various subtypes. High throughput technologies such as Deoxyribo Nucleic Acid (DNA) microarray are highly active in the field of cancer detection and classification alternatively. Objective: Yet, a precise analysis is important in microarray data applications as microarray experiments provide huge amount of data. Gene selection techniques promote microarray usage in the field of medicine. The objective of gene selection is to select a small subset of genes, which are the most informative in classification. associations while known disease-lncRNA associations are required only. Method: In this study, multi-objective evolutionary algorithm is used for gene subset selection in Leukemia classification. An initial redundant and irrelevant gene removal is followed by multiobjective evolutionary based gene subset selection. Gene subset selection highly influences the perfect classification. Thus, selecting the appropriate algorithm for subset selection is important. Results: The performance of the proposed method is compared against the standard genetic algorithm and evolutionary algorithm. Three Leukemia microarray datasets were used to evaluate the performance of the proposed method. Perfect classification was achieved for all the datasets only with few significant genes using the proposed approach. Conclusion: Thus, it is obvious that the proposed study perfectly classifies Leukemia with only few significant genes.</P>

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Incremental Search for Informative Gene Selection in Cancer Classification

Annals of Emerging Technologies in Computing ◽

10.33166/aetic.2021.02.002 ◽

2021 ◽

Vol 5 (2) ◽

pp. 15-21

Author(s):

Fathima Fajila ◽

Yuhanis Yusof

Keyword(s):

Data Analysis ◽

Microarray Data ◽

Gene Selection ◽

Subset Selection ◽

Cancer Classification ◽

Microarray Data Analysis ◽

Informative Gene ◽

Incremental Search ◽

Selection Approach ◽

Microarray Datasets

Although numerous methods of using microarray data analysis for classification have been reported, there is space in the field of cancer classification for new inventions in terms of informative gene selection. This study introduces a new incremental search-based gene selection approach for cancer classification. The strength of wrappers in determining relevant genes in a gene pool can be increased as they evaluate each possible gene’s subset. Nevertheless, the searching algorithms play a major role in gene’s subset selection. Hence, there is the possibility of finding more informative genes with incremental application. Thus, we introduce an approach which utilizes two searching algorithms in gene’s subset selection. The approach was efficient enough to classify five out of six microarray datasets with 100% accuracy using only a few biomarkers while the rest classified with only one misclassification.

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