Drug–target interaction prediction using unifying of graph regularized nuclear norm with bilinear factorization

Abstract Background Wet-lab experiments for identification of interactions between drugs and target proteins are time-consuming, costly and labor-intensive. The use of computational prediction of drug–target interactions (DTIs), which is one of the significant points in drug discovery, has been cons...

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Autores principales:	Ali Ghanbari Sorkhi, Zahra Abbasi, Majid Iranpour Mobarakeh, Jamshid Pirgazi
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Drug–target interaction Computational prediction Low-rank interaction Drug discovery Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/60571230ee8a46e59cc08fa5b83be312
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spelling	oai:doaj.org-article:60571230ee8a46e59cc08fa5b83be3122021-11-21T12:09:12ZDrug–target interaction prediction using unifying of graph regularized nuclear norm with bilinear factorization10.1186/s12859-021-04464-21471-2105https://doaj.org/article/60571230ee8a46e59cc08fa5b83be3122021-11-01T00:00:00Zhttps://doi.org/10.1186/s12859-021-04464-2https://doaj.org/toc/1471-2105Abstract Background Wet-lab experiments for identification of interactions between drugs and target proteins are time-consuming, costly and labor-intensive. The use of computational prediction of drug–target interactions (DTIs), which is one of the significant points in drug discovery, has been considered by many researchers in recent years. It also reduces the search space of interactions by proposing potential interaction candidates. Results In this paper, a new approach based on unifying matrix factorization and nuclear norm minimization is proposed to find a low-rank interaction. In this combined method, to solve the low-rank matrix approximation, the terms in the DTI problem are used in such a way that the nuclear norm regularized problem is optimized by a bilinear factorization based on Rank-Restricted Soft Singular Value Decomposition (RRSSVD). In the proposed method, adjacencies between drugs and targets are encoded by graphs. Drug–target interaction, drug-drug similarity, target-target, and combination of similarities have also been used as input. Conclusions The proposed method is evaluated on four benchmark datasets known as Enzymes (E), Ion channels (ICs), G protein-coupled receptors (GPCRs) and nuclear receptors (NRs) based on AUC, AUPR, and time measure. The results show an improvement in the performance of the proposed method compared to the state-of-the-art techniques.Ali Ghanbari SorkhiZahra AbbasiMajid Iranpour MobarakehJamshid PirgaziBMCarticleDrug–target interactionComputational predictionLow-rank interactionDrug discoveryComputer applications to medicine. Medical informaticsR858-859.7Biology (General)QH301-705.5ENBMC Bioinformatics, Vol 22, Iss 1, Pp 1-23 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Drug–target interaction Computational prediction Low-rank interaction Drug discovery Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5
spellingShingle	Drug–target interaction Computational prediction Low-rank interaction Drug discovery Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5 Ali Ghanbari Sorkhi Zahra Abbasi Majid Iranpour Mobarakeh Jamshid Pirgazi Drug–target interaction prediction using unifying of graph regularized nuclear norm with bilinear factorization
description	Abstract Background Wet-lab experiments for identification of interactions between drugs and target proteins are time-consuming, costly and labor-intensive. The use of computational prediction of drug–target interactions (DTIs), which is one of the significant points in drug discovery, has been considered by many researchers in recent years. It also reduces the search space of interactions by proposing potential interaction candidates. Results In this paper, a new approach based on unifying matrix factorization and nuclear norm minimization is proposed to find a low-rank interaction. In this combined method, to solve the low-rank matrix approximation, the terms in the DTI problem are used in such a way that the nuclear norm regularized problem is optimized by a bilinear factorization based on Rank-Restricted Soft Singular Value Decomposition (RRSSVD). In the proposed method, adjacencies between drugs and targets are encoded by graphs. Drug–target interaction, drug-drug similarity, target-target, and combination of similarities have also been used as input. Conclusions The proposed method is evaluated on four benchmark datasets known as Enzymes (E), Ion channels (ICs), G protein-coupled receptors (GPCRs) and nuclear receptors (NRs) based on AUC, AUPR, and time measure. The results show an improvement in the performance of the proposed method compared to the state-of-the-art techniques.
format	article
author	Ali Ghanbari Sorkhi Zahra Abbasi Majid Iranpour Mobarakeh Jamshid Pirgazi
author_facet	Ali Ghanbari Sorkhi Zahra Abbasi Majid Iranpour Mobarakeh Jamshid Pirgazi
author_sort	Ali Ghanbari Sorkhi
title	Drug–target interaction prediction using unifying of graph regularized nuclear norm with bilinear factorization
title_short	Drug–target interaction prediction using unifying of graph regularized nuclear norm with bilinear factorization
title_full	Drug–target interaction prediction using unifying of graph regularized nuclear norm with bilinear factorization
title_fullStr	Drug–target interaction prediction using unifying of graph regularized nuclear norm with bilinear factorization
title_full_unstemmed	Drug–target interaction prediction using unifying of graph regularized nuclear norm with bilinear factorization
title_sort	drug–target interaction prediction using unifying of graph regularized nuclear norm with bilinear factorization
publisher	BMC
publishDate	2021
url	https://doaj.org/article/60571230ee8a46e59cc08fa5b83be312
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Drug–target interaction prediction using unifying of graph regularized nuclear norm with bilinear factorization

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