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>
The Effect of Evolutionary Algorithm in Gene Subset Selection for Cancer Classification
