Výsledky vyhledávání - "Rawan S. Olayan"

Akademický článek

DTi2Vec: Drug–target interaction prediction using network embedding and ensemble learning

Autor: Maha A. Thafar, Rawan S. Olayan, Somayah Albaradei, Vladimir B. Bajic, Takashi Gojobori, Magbubah Essack, Xin Gao

Publikováno v: Journal of Cheminformatics, Vol 13, Iss 1, Pp 1-18 (2021)

Abstract Drug–target interaction (DTI) prediction is a crucial step in drug discovery and repositioning as it reduces experimental validation costs if done right. Thus, developing in-silico methods to predict potential DTI has become a competitive

Externí odkaz: https://doaj.org/article/df451b2b7c4e484fab56585276a745fd

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Akademický článek

DTiGEMS+: drug–target interaction prediction using graph embedding, graph mining, and similarity-based techniques

Autor: Maha A. Thafar, Rawan S. Olayan, Haitham Ashoor, Somayah Albaradei, Vladimir B. Bajic, Xin Gao, Takashi Gojobori, Magbubah Essack

Publikováno v: Journal of Cheminformatics, Vol 12, Iss 1, Pp 1-17 (2020)

Abstract In silico prediction of drug–target interactions is a critical phase in the sustainable drug development process, especially when the research focus is to capitalize on the repositioning of existing drugs. However, developing such computat

Externí odkaz: https://doaj.org/article/9b0584ac1995479186dd701e130dba65

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DTiGEMS+: drug–target interaction prediction using graph embedding, graph mining, and similarity-based techniques

Autor: Magbubah Essack, Haitham Ashoor, Maha A. Thafar, Somayah Albaradei, Vladimir B. Bajic, Xin Gao, Takashi Gojobori, Rawan S. Olayan

Publikováno v: Journal of Cheminformatics
Journal of Cheminformatics, Vol 12, Iss 1, Pp 1-17 (2020)

In silico prediction of drug–target interactions is a critical phase in the sustainable drug development process, especially when the research focus is to capitalize on the repositioning of existing drugs. However, developing such computational met

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f01a7cb144e63848d6bda29ac5807a4d
http://europepmc.org/articles/PMC7325230

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DDR: efficient computational method to predict drug–target interactions using graph mining and machine learning approaches

Autor: Rawan S. Olayan, Haitham Ashoor, Vladimir B. Bajic

Publikováno v: Bioinformatics

Motivation Finding computationally drug–target interactions (DTIs) is a convenient strategy to identify new DTIs at low cost with reasonable accuracy. However, the current DTI prediction methods suffer the high false positive prediction rate. Resul

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::33007adb0e5d9760d790b873477f1c9e
https://doi.org/10.1093/bioinformatics/bty417

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