Microarray cancer feature selection: Review, challenges and research directions
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Date
2020
Journal Title
Journal ISSN
Volume Title
Publisher
International Journal of Cognitive Computing in Engineering (IJCCE), Elsevier B. V.
Abstract
Microarray technology has become an emerging trend in the domain of genetic research in which many researchers
employ to study and investigate the levels of genes’ expression in a given organism. Microarray experiments
have lots of application areas in the health sector such as diseases prediction and diagnosis, cancer study
and soon. The enormous quantity of raw gene expression data usually results in analytical and computational
complexities which include feature selection and classification of the datasets into the correct class or group. To
achieve satisfactory cancer classification accuracy with the complete set of genes remains a great challenge, due
to the high dimensions, small sample size, and presence of noise in gene expression data. Feature reduction is
critical and sensitive in the classification task. Therefore, this paper presents a comprehensive survey of studies
on microarray cancer classification with a focus on feature selection methods. In this paper, the taxonomy of the
various feature selection methods used for microarray cancer classification and open research issues have been
extensively discussed.
Description
Keywords
Feature selection, Filter Wrapper, Embedded, Microarray technology, Microarray data