DrugHybrid_BS: Using Hybrid Feature Combined With Bagging-SVM to Predict Potentially Druggable Proteins

Drug targets are biological macromolecules or biomolecule structures capable of specifically binding a therapeutic effect with a particular drug or regulating physiological functions. Due to the important value and role of drug targets in recent years, the prediction of potential drug targets has be...

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Autores principales:	Yuxin Gong, Bo Liao, Peng Wang, Quan Zou
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	monoDiKGap CC GAAC bagging support vector machine Therapeutics. Pharmacology RM1-950
Acceso en línea:	https://doaj.org/article/82de434ac7cc4768ae4a885693cc71a6
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spelling	oai:doaj.org-article:82de434ac7cc4768ae4a885693cc71a62021-12-01T17:00:28ZDrugHybrid_BS: Using Hybrid Feature Combined With Bagging-SVM to Predict Potentially Druggable Proteins1663-981210.3389/fphar.2021.771808https://doaj.org/article/82de434ac7cc4768ae4a885693cc71a62021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fphar.2021.771808/fullhttps://doaj.org/toc/1663-9812Drug targets are biological macromolecules or biomolecule structures capable of specifically binding a therapeutic effect with a particular drug or regulating physiological functions. Due to the important value and role of drug targets in recent years, the prediction of potential drug targets has become a research hotspot. The key to the research and development of modern new drugs is first to identify potential drug targets. In this paper, a new predictor, DrugHybrid_BS, is developed based on hybrid features and Bagging-SVM to identify potentially druggable proteins. This method combines the three features of monoDiKGap (k = 2), cross-covariance, and grouped amino acid composition. It removes redundant features and analyses key features through MRMD and MRMD2.0. The cross-validation results show that 96.9944% of the potentially druggable proteins can be accurately identified, and the accuracy of the independent test set has reached 96.5665%. This all means that DrugHybrid_BS has the potential to become a useful predictive tool for druggable proteins. In addition, the hybrid key features can identify 80.0343% of the potentially druggable proteins combined with Bagging-SVM, which indicates the significance of this part of the features for research.Yuxin GongYuxin GongYuxin GongBo LiaoBo LiaoBo LiaoPeng WangPeng WangPeng WangQuan ZouFrontiers Media S.A.articlemonoDiKGapCCGAACbaggingsupport vector machineTherapeutics. PharmacologyRM1-950ENFrontiers in Pharmacology, Vol 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	monoDiKGap CC GAAC bagging support vector machine Therapeutics. Pharmacology RM1-950
spellingShingle	monoDiKGap CC GAAC bagging support vector machine Therapeutics. Pharmacology RM1-950 Yuxin Gong Yuxin Gong Yuxin Gong Bo Liao Bo Liao Bo Liao Peng Wang Peng Wang Peng Wang Quan Zou DrugHybrid_BS: Using Hybrid Feature Combined With Bagging-SVM to Predict Potentially Druggable Proteins
description	Drug targets are biological macromolecules or biomolecule structures capable of specifically binding a therapeutic effect with a particular drug or regulating physiological functions. Due to the important value and role of drug targets in recent years, the prediction of potential drug targets has become a research hotspot. The key to the research and development of modern new drugs is first to identify potential drug targets. In this paper, a new predictor, DrugHybrid_BS, is developed based on hybrid features and Bagging-SVM to identify potentially druggable proteins. This method combines the three features of monoDiKGap (k = 2), cross-covariance, and grouped amino acid composition. It removes redundant features and analyses key features through MRMD and MRMD2.0. The cross-validation results show that 96.9944% of the potentially druggable proteins can be accurately identified, and the accuracy of the independent test set has reached 96.5665%. This all means that DrugHybrid_BS has the potential to become a useful predictive tool for druggable proteins. In addition, the hybrid key features can identify 80.0343% of the potentially druggable proteins combined with Bagging-SVM, which indicates the significance of this part of the features for research.
format	article
author	Yuxin Gong Yuxin Gong Yuxin Gong Bo Liao Bo Liao Bo Liao Peng Wang Peng Wang Peng Wang Quan Zou
author_facet	Yuxin Gong Yuxin Gong Yuxin Gong Bo Liao Bo Liao Bo Liao Peng Wang Peng Wang Peng Wang Quan Zou
author_sort	Yuxin Gong
title	DrugHybrid_BS: Using Hybrid Feature Combined With Bagging-SVM to Predict Potentially Druggable Proteins
title_short	DrugHybrid_BS: Using Hybrid Feature Combined With Bagging-SVM to Predict Potentially Druggable Proteins
title_full	DrugHybrid_BS: Using Hybrid Feature Combined With Bagging-SVM to Predict Potentially Druggable Proteins
title_fullStr	DrugHybrid_BS: Using Hybrid Feature Combined With Bagging-SVM to Predict Potentially Druggable Proteins
title_full_unstemmed	DrugHybrid_BS: Using Hybrid Feature Combined With Bagging-SVM to Predict Potentially Druggable Proteins
title_sort	drughybrid_bs: using hybrid feature combined with bagging-svm to predict potentially druggable proteins
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/82de434ac7cc4768ae4a885693cc71a6
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DrugHybrid_BS: Using Hybrid Feature Combined With Bagging-SVM to Predict Potentially Druggable Proteins

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