Feature selection methods on gene expression microarray data for cancer classification: A systematic review

This systematic review provides researchers interested in feature selection (FS) for processing microarray data with comprehensive information about the main research directions for gene expression classification conducted during the recent seven years. A set of 132 researches published by three different publishers is reviewed. The studied papers are categorized into nine directions based on their objectives. The FS directions that received various levels of attention were then summarized. The review revealed that 'propose hybrid FS methods' represented the most interesting research direction with a percentage of 34.9%, while the other directions have lower percentages that ranged from 13.6% down to 3%. This guides researchers to select the most competitive research direction. Papers in each category are thoroughly reviewed based on six perspectives, mainly: method (s), classifier(s), dataset(s), dataset dimension(s) range, performance metric(s), and result(s) achieved.

Automated summary

This systematic review provides comprehensive information on the main research directions for gene expression classification in the past seven years, with a focus on feature selection. The review identified 'propose hybrid FS methods' as the most interesting research direction, guiding researchers to select competitive research areas based on the findings. The papers were thoroughly reviewed based on six perspectives to provide insights for further research in this field.