Gene masking - a technique to improve accuracy for cancer classification with high dimensionality in microarray data

Abstract

BackgroundHigh dimensional feature space generally degrades classification in several applications. In this paper, we propose a strategy called gene masking, in which non-contributing dimensions are heuristically removed from the data to improve classification accuracy.MethodsGene masking is implemented via a binary encoded genetic algorithm that can be integrated seamlessly with classifiers during the training phase of classification to perform feature selection. It can also be used to discriminate between features that contribute most to the classification, thereby, allowing researchers to isolate features that may have special significance.ResultsThis technique was applied on publicly available datasets whereby it substantially reduced the number of features used for classification while maintaining high accuracies.ConclusionThe proposed technique can be extremely useful in feature selection as it heuristically removes non-contributing features to improve the performance of classifiers.

A transcriptomic study for identifying cardia‐ and non–cardia‐specific gastric cancer prognostic factors using genetic algorithm‐based methods

“…[14][15][16] Several researchers have applied GA-based machine learning methods to solve a variety of complex problems in high-dimensional omics data. 11,17 Thus, this study proposed two approaches that combine SVM and Cox models with a GA to explore an optimal subset of site-specific GC prognostic biomarkers. As a result, we finally identified 10 and 13 cardia-and non-cardia-specific GC prognostic factors with a good discriminatory ability, reflecting the GA-based algorithms' superior performance.…”

“…GA process, including natural selection, crossover and mutation, is a heuristic algorithm used to explore an optimal solution to a complex problem (such as non‐linear condition) 14‐16 . Several researchers have applied GA‐based machine learning methods to solve a variety of complex problems in high‐dimensional omics data 11,17 . Thus, this study proposed two approaches that combine SVM and Cox models with a GA to explore an optimal subset of site‐specific GC prognostic biomarkers.…”

“…Genetic algorithm (GA), a searching algorithm based on natural selection, crossover and mutation, has been reported to be a very efficient method for feature selection 9 . Several studies have demonstrated that GA‐based features selection methods can significantly improve the predictive accuracy of diseases risk prediction models 10,11 …”

“…9 Several studies have demonstrated that GA-based features selection methods can significantly improve the predictive accuracy of diseases risk prediction models. 10,11 In this study, to identify potential cardia-and non-cardia-specific GC prognostic biomarkers, we performed a comprehensive analysis using GA-based support vector machine (GA-SVM) and GA-based Cox regression method (GA-Cox) in the Cancer Genome Atlas (TCGA) stomach adenocarcinoma (STAD) transcriptomic data.…”

A Deep Learning and Genetic Algorithm Based Feature Selection Processes on Leukemia Data

“…Saini et al [20] proposed a gene masking derived from the genetic algorithm. An optimal gene mask is searched that provides the largest performance gain by removing the largest number of features for the chosen classification algorithm.…”