The Performance of Bio-Inspired Evolutionary Gene Selection Methods for Cancer Classification Using Microarray Dataset

Abstract

Abstract-Microarray based gene expression profiling has become an important and promising dataset for cancer classification that are used for diagnosis and prognosis purposes. It is important to determine the informative genes that cause the cancer to improve early cancer diagnosis and to give effective chemotherapy treatment. Furthermore, find accurate gene selection method that reduce the dimensionality and select informative genes is very significant issue in cancer classification area. In literature, there are several gene selection methods for cancer classification using microarray dataset. However, most of them did not concern on identifying minimum number of informative genes with high classification accuracy. Therefore, in our research study we discuss the performance of Bio-Inspired evolutionary gene selection method in cancer classification using microarray dataset. And, we prove that the Bio-Inspired evolutionary gene selection methods have superior classification accuracy with minimum number of selected genes.

A novel microarray gene selection and classification using intelligent dynamic grey wolf optimization

“…As of late, in researching cancer diseases, numerous ways have been opened up by the innovations of microarray utilizing gene expressions (ALSHAMLAN et al, 2014) .The huge number of gene expression levels are quantified in a single chip using Microarray. The microarray comprises of up to 6000 spots and measuring the area of 2cm by 2cm (SALOME et al, 2011).…”

Microarray Gene Expression Data Pre-Processing Using Ppca and Classification Using Rf-SVM Algorithm

“…In addition, they permit searching the solution space by means of considering more than one attribute simultaneously [8]. However, as other evolutionary schemes, the ABC has certain challenging issues, particularly in computational efficiency, when it is processed on complex and high-dimensional data like microarray datasets.…”

“…This hybrid gene selection provides a better balance between filters and wrapper gene selection schemes, being more computationally effective, as in filter schemes, and model feature dependencies as in wrapper schemes [8].…”

“…Saraswathi et al (2011) [13] Bioinspired evolutionary schemes are more appropriate and precise than the wrapper gene selection scheme [8] since they have the capability for searching and discovering the optimal or near-optimal solutions on high-dimensional solution spaces. In addition, they permit searching the solution space by means of considering more than one attribute simultaneously [8].…”

Artificial neural network classification of microarray data using new hybrid gene selection method

“…On the other hand, bio-inspired and evolutionary algorithms have been widely applied for genes selection that classify the disease with best accuracy (El Akadi et al, 2009;Alshamlan et al, 2015;Alshamlan et al, 2014;Yu et al, 2013b). A non-parallel plane proximal classifier is described in Ghorai et al (2011), where the authors used genetic algorithms for selecting genes for a cancer classification and the results are compared against an SVM.…”