A graph-based gene selection method for medical diagnosis problems using a many-objective PSO algorithm

Abstract Background Gene expression data play an important role in bioinformatics applications. Although there may be a large number of features in such data, they mainly tend to contain only a few samples. This can negatively impact the performance of data mining and machine learning algorithms. On...

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Autores principales:	Saeid Azadifar, Ali Ahmadi
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Gene selection Dimension reduction Many-objective PSO Gene clustering High dimensional Repair operator Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/4f6d1f5c1a824aa78b4cbd82b91abf86
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spelling	oai:doaj.org-article:4f6d1f5c1a824aa78b4cbd82b91abf862021-11-28T12:26:14ZA graph-based gene selection method for medical diagnosis problems using a many-objective PSO algorithm10.1186/s12911-021-01696-31472-6947https://doaj.org/article/4f6d1f5c1a824aa78b4cbd82b91abf862021-11-01T00:00:00Zhttps://doi.org/10.1186/s12911-021-01696-3https://doaj.org/toc/1472-6947Abstract Background Gene expression data play an important role in bioinformatics applications. Although there may be a large number of features in such data, they mainly tend to contain only a few samples. This can negatively impact the performance of data mining and machine learning algorithms. One of the most effective approaches to alleviate this problem is to use gene selection methods. The aim of gene selection is to reduce the dimensions (features) of gene expression data leading to eliminating irrelevant and redundant genes. Methods This paper presents a hybrid gene selection method based on graph theory and a many-objective particle swarm optimization (PSO) algorithm. To this end, a filter method is first utilized to reduce the initial space of the genes. Then, the gene space is represented as a graph to apply a graph clustering method to group the genes into several clusters. Moreover, the many-objective PSO algorithm is utilized to search an optimal subset of genes according to several criteria, which include classification error, node centrality, specificity, edge centrality, and the number of selected genes. A repair operator is proposed to cover the whole space of the genes and ensure that at least one gene is selected from each cluster. This leads to an increasement in the diversity of the selected genes. Results To evaluate the performance of the proposed method, extensive experiments are conducted based on seven datasets and two evaluation measures. In addition, three classifiers—Decision Tree (DT), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN)—are utilized to compare the effectiveness of the proposed gene selection method with other state-of-the-art methods. The results of these experiments demonstrate that our proposed method not only achieves more accurate classification, but also selects fewer genes than other methods. Conclusion This study shows that the proposed multi-objective PSO algorithm simultaneously removes irrelevant and redundant features using several different criteria. Also, the use of the clustering algorithm and the repair operator has improved the performance of the proposed method by covering the whole space of the problem.Saeid AzadifarAli AhmadiBMCarticleGene selectionDimension reductionMany-objective PSOGene clusteringHigh dimensionalRepair operatorComputer applications to medicine. Medical informaticsR858-859.7ENBMC Medical Informatics and Decision Making, Vol 21, Iss 1, Pp 1-16 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Gene selection Dimension reduction Many-objective PSO Gene clustering High dimensional Repair operator Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Gene selection Dimension reduction Many-objective PSO Gene clustering High dimensional Repair operator Computer applications to medicine. Medical informatics R858-859.7 Saeid Azadifar Ali Ahmadi A graph-based gene selection method for medical diagnosis problems using a many-objective PSO algorithm
description	Abstract Background Gene expression data play an important role in bioinformatics applications. Although there may be a large number of features in such data, they mainly tend to contain only a few samples. This can negatively impact the performance of data mining and machine learning algorithms. One of the most effective approaches to alleviate this problem is to use gene selection methods. The aim of gene selection is to reduce the dimensions (features) of gene expression data leading to eliminating irrelevant and redundant genes. Methods This paper presents a hybrid gene selection method based on graph theory and a many-objective particle swarm optimization (PSO) algorithm. To this end, a filter method is first utilized to reduce the initial space of the genes. Then, the gene space is represented as a graph to apply a graph clustering method to group the genes into several clusters. Moreover, the many-objective PSO algorithm is utilized to search an optimal subset of genes according to several criteria, which include classification error, node centrality, specificity, edge centrality, and the number of selected genes. A repair operator is proposed to cover the whole space of the genes and ensure that at least one gene is selected from each cluster. This leads to an increasement in the diversity of the selected genes. Results To evaluate the performance of the proposed method, extensive experiments are conducted based on seven datasets and two evaluation measures. In addition, three classifiers—Decision Tree (DT), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN)—are utilized to compare the effectiveness of the proposed gene selection method with other state-of-the-art methods. The results of these experiments demonstrate that our proposed method not only achieves more accurate classification, but also selects fewer genes than other methods. Conclusion This study shows that the proposed multi-objective PSO algorithm simultaneously removes irrelevant and redundant features using several different criteria. Also, the use of the clustering algorithm and the repair operator has improved the performance of the proposed method by covering the whole space of the problem.
format	article
author	Saeid Azadifar Ali Ahmadi
author_facet	Saeid Azadifar Ali Ahmadi
author_sort	Saeid Azadifar
title	A graph-based gene selection method for medical diagnosis problems using a many-objective PSO algorithm
title_short	A graph-based gene selection method for medical diagnosis problems using a many-objective PSO algorithm
title_full	A graph-based gene selection method for medical diagnosis problems using a many-objective PSO algorithm
title_fullStr	A graph-based gene selection method for medical diagnosis problems using a many-objective PSO algorithm
title_full_unstemmed	A graph-based gene selection method for medical diagnosis problems using a many-objective PSO algorithm
title_sort	graph-based gene selection method for medical diagnosis problems using a many-objective pso algorithm
publisher	BMC
publishDate	2021
url	https://doaj.org/article/4f6d1f5c1a824aa78b4cbd82b91abf86
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A graph-based gene selection method for medical diagnosis problems using a many-objective PSO algorithm

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