A robust and stable gene selection algorithm based on graph theory and machine learning

Abstract Background Nowadays we are observing an explosion of gene expression data with phenotypes. It enables us to accurately identify genes responsible for certain medical condition as well as classify them for drug target. Like any other phenotype data in medical domain, gene expression data wit...

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Autores principales:	Subrata Saha, Ahmed Soliman, Sanguthevar Rajasekaran
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Robust and Stable Gene Selection Algorithm (RSGSA) Symmetric Uncertainty (SU) Gain ratio (GR) Support vector machine-recursive feature elimination (SVM-RFE) Linear Support Vector Machine (LSVM) Medicine R Genetics QH426-470
Acceso en línea:	https://doaj.org/article/21e4f9da095a4dce8150adf6a5967f79
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spelling	oai:doaj.org-article:21e4f9da095a4dce8150adf6a5967f792021-11-14T12:15:35ZA robust and stable gene selection algorithm based on graph theory and machine learning10.1186/s40246-021-00366-91479-7364https://doaj.org/article/21e4f9da095a4dce8150adf6a5967f792021-11-01T00:00:00Zhttps://doi.org/10.1186/s40246-021-00366-9https://doaj.org/toc/1479-7364Abstract Background Nowadays we are observing an explosion of gene expression data with phenotypes. It enables us to accurately identify genes responsible for certain medical condition as well as classify them for drug target. Like any other phenotype data in medical domain, gene expression data with phenotypes also suffer from being a very underdetermined system. In a very large set of features but a very small sample size domain (e.g. DNA microarray, RNA-seq data, GWAS data, etc.), it is often reported that several contrasting feature subsets may yield near equally optimal results. This phenomenon is known as instability. Considering these facts, we have developed a robust and stable supervised gene selection algorithm to select a set of robust and stable genes having a better prediction ability from the gene expression datasets with phenotypes. Stability and robustness is ensured by class and instance level perturbations, respectively. Results We have performed rigorous experimental evaluations using 10 real gene expression microarray datasets with phenotypes. They reveal that our algorithm outperforms the state-of-the-art algorithms with respect to stability and classification accuracy. We have also performed biological enrichment analysis based on gene ontology-biological processes (GO-BP) terms, disease ontology (DO) terms, and biological pathways. Conclusions It is indisputable from the results of the performance evaluations that our proposed method is indeed an effective and efficient supervised gene selection algorithm.Subrata SahaAhmed SolimanSanguthevar RajasekaranBMCarticleRobust and Stable Gene Selection Algorithm (RSGSA)Symmetric Uncertainty (SU)Gain ratio (GR)Support vector machine-recursive feature elimination (SVM-RFE)Linear Support Vector Machine (LSVM)MedicineRGeneticsQH426-470ENHuman Genomics, Vol 15, Iss 1, Pp 1-16 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Robust and Stable Gene Selection Algorithm (RSGSA) Symmetric Uncertainty (SU) Gain ratio (GR) Support vector machine-recursive feature elimination (SVM-RFE) Linear Support Vector Machine (LSVM) Medicine R Genetics QH426-470
spellingShingle	Robust and Stable Gene Selection Algorithm (RSGSA) Symmetric Uncertainty (SU) Gain ratio (GR) Support vector machine-recursive feature elimination (SVM-RFE) Linear Support Vector Machine (LSVM) Medicine R Genetics QH426-470 Subrata Saha Ahmed Soliman Sanguthevar Rajasekaran A robust and stable gene selection algorithm based on graph theory and machine learning
description	Abstract Background Nowadays we are observing an explosion of gene expression data with phenotypes. It enables us to accurately identify genes responsible for certain medical condition as well as classify them for drug target. Like any other phenotype data in medical domain, gene expression data with phenotypes also suffer from being a very underdetermined system. In a very large set of features but a very small sample size domain (e.g. DNA microarray, RNA-seq data, GWAS data, etc.), it is often reported that several contrasting feature subsets may yield near equally optimal results. This phenomenon is known as instability. Considering these facts, we have developed a robust and stable supervised gene selection algorithm to select a set of robust and stable genes having a better prediction ability from the gene expression datasets with phenotypes. Stability and robustness is ensured by class and instance level perturbations, respectively. Results We have performed rigorous experimental evaluations using 10 real gene expression microarray datasets with phenotypes. They reveal that our algorithm outperforms the state-of-the-art algorithms with respect to stability and classification accuracy. We have also performed biological enrichment analysis based on gene ontology-biological processes (GO-BP) terms, disease ontology (DO) terms, and biological pathways. Conclusions It is indisputable from the results of the performance evaluations that our proposed method is indeed an effective and efficient supervised gene selection algorithm.
format	article
author	Subrata Saha Ahmed Soliman Sanguthevar Rajasekaran
author_facet	Subrata Saha Ahmed Soliman Sanguthevar Rajasekaran
author_sort	Subrata Saha
title	A robust and stable gene selection algorithm based on graph theory and machine learning
title_short	A robust and stable gene selection algorithm based on graph theory and machine learning
title_full	A robust and stable gene selection algorithm based on graph theory and machine learning
title_fullStr	A robust and stable gene selection algorithm based on graph theory and machine learning
title_full_unstemmed	A robust and stable gene selection algorithm based on graph theory and machine learning
title_sort	robust and stable gene selection algorithm based on graph theory and machine learning
publisher	BMC
publishDate	2021
url	https://doaj.org/article/21e4f9da095a4dce8150adf6a5967f79
work_keys_str_mv	AT subratasaha arobustandstablegeneselectionalgorithmbasedongraphtheoryandmachinelearning AT ahmedsoliman arobustandstablegeneselectionalgorithmbasedongraphtheoryandmachinelearning AT sanguthevarrajasekaran arobustandstablegeneselectionalgorithmbasedongraphtheoryandmachinelearning AT subratasaha robustandstablegeneselectionalgorithmbasedongraphtheoryandmachinelearning AT ahmedsoliman robustandstablegeneselectionalgorithmbasedongraphtheoryandmachinelearning AT sanguthevarrajasekaran robustandstablegeneselectionalgorithmbasedongraphtheoryandmachinelearning
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A robust and stable gene selection algorithm based on graph theory and machine learning

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