Screening drug-target interactions with positive-unlabeled learning

Abstract Identifying drug-target interaction (DTI) candidates is crucial for drug repositioning. However, usually only positive DTIs are deposited in known databases, which challenges computational methods to predict novel DTIs due to the lack of negative samples. To overcome this dilemma, researche...

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Autores principales:	Lihong Peng, Wen Zhu, Bo Liao, Yu Duan, Min Chen, Yi Chen, Jialiang Yang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/656e1bfed72c43d39dedc4ea57292f76
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spelling	oai:doaj.org-article:656e1bfed72c43d39dedc4ea57292f762021-12-02T11:40:14ZScreening drug-target interactions with positive-unlabeled learning10.1038/s41598-017-08079-72045-2322https://doaj.org/article/656e1bfed72c43d39dedc4ea57292f762017-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-08079-7https://doaj.org/toc/2045-2322Abstract Identifying drug-target interaction (DTI) candidates is crucial for drug repositioning. However, usually only positive DTIs are deposited in known databases, which challenges computational methods to predict novel DTIs due to the lack of negative samples. To overcome this dilemma, researchers usually randomly select negative samples from unlabeled drug-target pairs, which introduces a lot of false-positives. In this study, a negative sample extraction method named NDTISE is first developed to screen strong negative DTI examples based on positive-unlabeled learning. A novel DTI screening framework, PUDTI, is then designed to infer new drug repositioning candidates by integrating NDTISE, probabilities that remaining ambiguous samples belong to the positive and negative classes, and an SVM-based optimization model. We investigated the effectiveness of NDTISE on a DTI data provided by NCPIS. NDTISE is much better than random selection and slightly outperforms NCPIS. We then compared PUDTI with 6 state-of-the-art methods on 4 classes of DTI datasets from human enzymes, ion channels, GPCRs and nuclear receptors. PUDTI achieved the highest AUC among the 7 methods on all 4 datasets. Finally, we validated a few top predicted DTIs through mining independent drug databases and literatures. In conclusion, PUDTI provides an effective pre-filtering method for new drug design.Lihong PengWen ZhuBo LiaoYu DuanMin ChenYi ChenJialiang YangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-17 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Lihong Peng Wen Zhu Bo Liao Yu Duan Min Chen Yi Chen Jialiang Yang Screening drug-target interactions with positive-unlabeled learning
description	Abstract Identifying drug-target interaction (DTI) candidates is crucial for drug repositioning. However, usually only positive DTIs are deposited in known databases, which challenges computational methods to predict novel DTIs due to the lack of negative samples. To overcome this dilemma, researchers usually randomly select negative samples from unlabeled drug-target pairs, which introduces a lot of false-positives. In this study, a negative sample extraction method named NDTISE is first developed to screen strong negative DTI examples based on positive-unlabeled learning. A novel DTI screening framework, PUDTI, is then designed to infer new drug repositioning candidates by integrating NDTISE, probabilities that remaining ambiguous samples belong to the positive and negative classes, and an SVM-based optimization model. We investigated the effectiveness of NDTISE on a DTI data provided by NCPIS. NDTISE is much better than random selection and slightly outperforms NCPIS. We then compared PUDTI with 6 state-of-the-art methods on 4 classes of DTI datasets from human enzymes, ion channels, GPCRs and nuclear receptors. PUDTI achieved the highest AUC among the 7 methods on all 4 datasets. Finally, we validated a few top predicted DTIs through mining independent drug databases and literatures. In conclusion, PUDTI provides an effective pre-filtering method for new drug design.
format	article
author	Lihong Peng Wen Zhu Bo Liao Yu Duan Min Chen Yi Chen Jialiang Yang
author_facet	Lihong Peng Wen Zhu Bo Liao Yu Duan Min Chen Yi Chen Jialiang Yang
author_sort	Lihong Peng
title	Screening drug-target interactions with positive-unlabeled learning
title_short	Screening drug-target interactions with positive-unlabeled learning
title_full	Screening drug-target interactions with positive-unlabeled learning
title_fullStr	Screening drug-target interactions with positive-unlabeled learning
title_full_unstemmed	Screening drug-target interactions with positive-unlabeled learning
title_sort	screening drug-target interactions with positive-unlabeled learning
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/656e1bfed72c43d39dedc4ea57292f76
work_keys_str_mv	AT lihongpeng screeningdrugtargetinteractionswithpositiveunlabeledlearning AT wenzhu screeningdrugtargetinteractionswithpositiveunlabeledlearning AT boliao screeningdrugtargetinteractionswithpositiveunlabeledlearning AT yuduan screeningdrugtargetinteractionswithpositiveunlabeledlearning AT minchen screeningdrugtargetinteractionswithpositiveunlabeledlearning AT yichen screeningdrugtargetinteractionswithpositiveunlabeledlearning AT jialiangyang screeningdrugtargetinteractionswithpositiveunlabeledlearning
_version_	1718395646223843328

Screening drug-target interactions with positive-unlabeled learning

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