Construction of a Support Vector Machine–Based Classifier for Pulmonary Arterial Hypertension Patients

Pulmonary arterial hypertension (PAH) is a disease leading to right heart failure and death due to increased pulmonary arterial tension and vascular resistance. So far, PAH has not been fully understood, and current treatments are much limited. Gene expression profiles of healthy people and PAH pati...

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Autores principales:	Zhenglu Shang, Jiashun Sun, Jingjiao Hui, Yanhua Yu, Xiaoyun Bian, Bowen Yang, Kewu Deng, Li Lin
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	pulmonary arterial hypertension SVM-RFE classifier biomarker Na Genetics QH426-470
Acceso en línea:	https://doaj.org/article/91d220df6c9649259595922caf577ee6
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id	oai:doaj.org-article:91d220df6c9649259595922caf577ee6
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spelling	oai:doaj.org-article:91d220df6c9649259595922caf577ee62021-11-30T10:06:50ZConstruction of a Support Vector Machine–Based Classifier for Pulmonary Arterial Hypertension Patients1664-802110.3389/fgene.2021.781011https://doaj.org/article/91d220df6c9649259595922caf577ee62021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fgene.2021.781011/fullhttps://doaj.org/toc/1664-8021Pulmonary arterial hypertension (PAH) is a disease leading to right heart failure and death due to increased pulmonary arterial tension and vascular resistance. So far, PAH has not been fully understood, and current treatments are much limited. Gene expression profiles of healthy people and PAH patients in GSE33463 dataset were analyzed in this study. Then 110 differentially expressed genes (DEGs) were obtained. Afterward, the PPI network based on DEGs was constructed, followed by the analysis of functional modules, whose results showed that the genes in the major function modules significantly enriched in immune-related functions. Moreover, four optimal feature genes were screened from the DEGs by support vector machine–recursive feature elimination (SVM-RFE) algorithm (EPB42, IFIT2, FOSB, and SNF1LK). The receiver operating characteristic curve showed that the SVM classifier based on optimal feature genes could effectively distinguish healthy people from PAH patients. Last, the expression of optimal feature genes was analyzed in the GSE33463 dataset and clinical samples. It was found that EPB42 and IFIT2 were highly expressed in PAH patients, while FOSB and SNF1LK were lowly expressed. In conclusion, the four optimal feature genes screened here are potential biomarkers for PAH and are expected to be used in early diagnosis for PAH.Zhenglu ShangJiashun SunJingjiao HuiYanhua YuXiaoyun BianBowen YangKewu DengLi LinFrontiers Media S.A.articlepulmonary arterial hypertensionSVM-RFEclassifierbiomarkerNaGeneticsQH426-470ENFrontiers in Genetics, Vol 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	pulmonary arterial hypertension SVM-RFE classifier biomarker Na Genetics QH426-470
spellingShingle	pulmonary arterial hypertension SVM-RFE classifier biomarker Na Genetics QH426-470 Zhenglu Shang Jiashun Sun Jingjiao Hui Yanhua Yu Xiaoyun Bian Bowen Yang Kewu Deng Li Lin Construction of a Support Vector Machine–Based Classifier for Pulmonary Arterial Hypertension Patients
description	Pulmonary arterial hypertension (PAH) is a disease leading to right heart failure and death due to increased pulmonary arterial tension and vascular resistance. So far, PAH has not been fully understood, and current treatments are much limited. Gene expression profiles of healthy people and PAH patients in GSE33463 dataset were analyzed in this study. Then 110 differentially expressed genes (DEGs) were obtained. Afterward, the PPI network based on DEGs was constructed, followed by the analysis of functional modules, whose results showed that the genes in the major function modules significantly enriched in immune-related functions. Moreover, four optimal feature genes were screened from the DEGs by support vector machine–recursive feature elimination (SVM-RFE) algorithm (EPB42, IFIT2, FOSB, and SNF1LK). The receiver operating characteristic curve showed that the SVM classifier based on optimal feature genes could effectively distinguish healthy people from PAH patients. Last, the expression of optimal feature genes was analyzed in the GSE33463 dataset and clinical samples. It was found that EPB42 and IFIT2 were highly expressed in PAH patients, while FOSB and SNF1LK were lowly expressed. In conclusion, the four optimal feature genes screened here are potential biomarkers for PAH and are expected to be used in early diagnosis for PAH.
format	article
author	Zhenglu Shang Jiashun Sun Jingjiao Hui Yanhua Yu Xiaoyun Bian Bowen Yang Kewu Deng Li Lin
author_facet	Zhenglu Shang Jiashun Sun Jingjiao Hui Yanhua Yu Xiaoyun Bian Bowen Yang Kewu Deng Li Lin
author_sort	Zhenglu Shang
title	Construction of a Support Vector Machine–Based Classifier for Pulmonary Arterial Hypertension Patients
title_short	Construction of a Support Vector Machine–Based Classifier for Pulmonary Arterial Hypertension Patients
title_full	Construction of a Support Vector Machine–Based Classifier for Pulmonary Arterial Hypertension Patients
title_fullStr	Construction of a Support Vector Machine–Based Classifier for Pulmonary Arterial Hypertension Patients
title_full_unstemmed	Construction of a Support Vector Machine–Based Classifier for Pulmonary Arterial Hypertension Patients
title_sort	construction of a support vector machine–based classifier for pulmonary arterial hypertension patients
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/91d220df6c9649259595922caf577ee6
work_keys_str_mv	AT zhenglushang constructionofasupportvectormachinebasedclassifierforpulmonaryarterialhypertensionpatients AT jiashunsun constructionofasupportvectormachinebasedclassifierforpulmonaryarterialhypertensionpatients AT jingjiaohui constructionofasupportvectormachinebasedclassifierforpulmonaryarterialhypertensionpatients AT yanhuayu constructionofasupportvectormachinebasedclassifierforpulmonaryarterialhypertensionpatients AT xiaoyunbian constructionofasupportvectormachinebasedclassifierforpulmonaryarterialhypertensionpatients AT bowenyang constructionofasupportvectormachinebasedclassifierforpulmonaryarterialhypertensionpatients AT kewudeng constructionofasupportvectormachinebasedclassifierforpulmonaryarterialhypertensionpatients AT lilin constructionofasupportvectormachinebasedclassifierforpulmonaryarterialhypertensionpatients
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Construction of a Support Vector Machine–Based Classifier for Pulmonary Arterial Hypertension Patients

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