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BETA: a comprehensive benchmark for computational drug–target prediction

Internal validation is the most popular evaluation strategy used for drug–target predictive models. The simple random shuffling in the cross-validation, however, is not always ideal to handle large, diverse and copious datasets as it could potentially introduce bias. Hence, these predictive models c...

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Autores principales: Zong, Nansu, Li, Ning, Wen, Andrew, Ngo, Victoria, Yu, Yue, Huang, Ming, Chowdhury, Shaika, Jiang, Chao, Fu, Sunyang, Weinshilboum, Richard, Jiang, Guoqian, Hunter, Lawrence, Liu, Hongfang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9294420/
https://www.ncbi.nlm.nih.gov/pubmed/35649342
http://dx.doi.org/10.1093/bib/bbac199
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author Zong, Nansu
Li, Ning
Wen, Andrew
Ngo, Victoria
Yu, Yue
Huang, Ming
Chowdhury, Shaika
Jiang, Chao
Fu, Sunyang
Weinshilboum, Richard
Jiang, Guoqian
Hunter, Lawrence
Liu, Hongfang
author_facet Zong, Nansu
Li, Ning
Wen, Andrew
Ngo, Victoria
Yu, Yue
Huang, Ming
Chowdhury, Shaika
Jiang, Chao
Fu, Sunyang
Weinshilboum, Richard
Jiang, Guoqian
Hunter, Lawrence
Liu, Hongfang
author_sort Zong, Nansu
collection PubMed
description Internal validation is the most popular evaluation strategy used for drug–target predictive models. The simple random shuffling in the cross-validation, however, is not always ideal to handle large, diverse and copious datasets as it could potentially introduce bias. Hence, these predictive models cannot be comprehensively evaluated to provide insight into their general performance on a variety of use-cases (e.g. permutations of different levels of connectiveness and categories in drug and target space, as well as validations based on different data sources). In this work, we introduce a benchmark, BETA, that aims to address this gap by (i) providing an extensive multipartite network consisting of 0.97 million biomedical concepts and 8.5 million associations, in addition to 62 million drug–drug and protein–protein similarities and (ii) presenting evaluation strategies that reflect seven cases (i.e. general, screening with different connectivity, target and drug screening based on categories, searching for specific drugs and targets and drug repurposing for specific diseases), a total of seven Tests (consisting of 344 Tasks in total) across multiple sampling and validation strategies. Six state-of-the-art methods covering two broad input data types (chemical structure- and gene sequence-based and network-based) were tested across all the developed Tasks. The best-worst performing cases have been analyzed to demonstrate the ability of the proposed benchmark to identify limitations of the tested methods for running over the benchmark tasks. The results highlight BETA as a benchmark in the selection of computational strategies for drug repurposing and target discovery.
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spelling pubmed-92944202022-07-20 BETA: a comprehensive benchmark for computational drug–target prediction Zong, Nansu Li, Ning Wen, Andrew Ngo, Victoria Yu, Yue Huang, Ming Chowdhury, Shaika Jiang, Chao Fu, Sunyang Weinshilboum, Richard Jiang, Guoqian Hunter, Lawrence Liu, Hongfang Brief Bioinform Case Study Internal validation is the most popular evaluation strategy used for drug–target predictive models. The simple random shuffling in the cross-validation, however, is not always ideal to handle large, diverse and copious datasets as it could potentially introduce bias. Hence, these predictive models cannot be comprehensively evaluated to provide insight into their general performance on a variety of use-cases (e.g. permutations of different levels of connectiveness and categories in drug and target space, as well as validations based on different data sources). In this work, we introduce a benchmark, BETA, that aims to address this gap by (i) providing an extensive multipartite network consisting of 0.97 million biomedical concepts and 8.5 million associations, in addition to 62 million drug–drug and protein–protein similarities and (ii) presenting evaluation strategies that reflect seven cases (i.e. general, screening with different connectivity, target and drug screening based on categories, searching for specific drugs and targets and drug repurposing for specific diseases), a total of seven Tests (consisting of 344 Tasks in total) across multiple sampling and validation strategies. Six state-of-the-art methods covering two broad input data types (chemical structure- and gene sequence-based and network-based) were tested across all the developed Tasks. The best-worst performing cases have been analyzed to demonstrate the ability of the proposed benchmark to identify limitations of the tested methods for running over the benchmark tasks. The results highlight BETA as a benchmark in the selection of computational strategies for drug repurposing and target discovery. Oxford University Press 2022-06-02 /pmc/articles/PMC9294420/ /pubmed/35649342 http://dx.doi.org/10.1093/bib/bbac199 Text en © The Author(s) 2022. Published by Oxford University Press. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Case Study
Zong, Nansu
Li, Ning
Wen, Andrew
Ngo, Victoria
Yu, Yue
Huang, Ming
Chowdhury, Shaika
Jiang, Chao
Fu, Sunyang
Weinshilboum, Richard
Jiang, Guoqian
Hunter, Lawrence
Liu, Hongfang
BETA: a comprehensive benchmark for computational drug–target prediction
title BETA: a comprehensive benchmark for computational drug–target prediction
title_full BETA: a comprehensive benchmark for computational drug–target prediction
title_fullStr BETA: a comprehensive benchmark for computational drug–target prediction
title_full_unstemmed BETA: a comprehensive benchmark for computational drug–target prediction
title_short BETA: a comprehensive benchmark for computational drug–target prediction
title_sort beta: a comprehensive benchmark for computational drug–target prediction
topic Case Study
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9294420/
https://www.ncbi.nlm.nih.gov/pubmed/35649342
http://dx.doi.org/10.1093/bib/bbac199
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