SYNPRED: prediction of drug combination effects in cancer using different synergy metrics and ensemble learning

Detalhes bibliográficos
Autor(a) principal: Preto, Antonio J.
Data de Publicação: 2022
Outros Autores: Matos-Filipe, Pedro, Mourão, Joana, Moreira, Irina S.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10316/103273
https://doi.org/10.1093/gigascience/giac087
Resumo: In cancer research, high-throughput screening technologies produce large amounts of multiomics data from different populations and cell types. However, analysis of such data encounters difficulties due to disease heterogeneity, further exacerbated by human biological complexity and genomic variability. The specific profile of cancer as a disease (or, more realistically, a set of diseases) urges the development of approaches that maximize the effect while minimizing the dosage of drugs. Now is the time to redefine the approach to drug discovery, bringing an artificial intelligence (AI)-powered informational view that integrates the relevant scientific fields and explores new territories.
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spelling SYNPRED: prediction of drug combination effects in cancer using different synergy metrics and ensemble learningbiophysicscancerdrug synergyensemble learninginterpretabilityomicsArtificial IntelligenceBenchmarkingDrug CombinationsHumansMachine LearningAntineoplastic AgentsNeoplasmsIn cancer research, high-throughput screening technologies produce large amounts of multiomics data from different populations and cell types. However, analysis of such data encounters difficulties due to disease heterogeneity, further exacerbated by human biological complexity and genomic variability. The specific profile of cancer as a disease (or, more realistically, a set of diseases) urges the development of approaches that maximize the effect while minimizing the dosage of drugs. Now is the time to redefine the approach to drug discovery, bringing an artificial intelligence (AI)-powered informational view that integrates the relevant scientific fields and explores new territories.Oxford University Press2022info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/103273http://hdl.handle.net/10316/103273https://doi.org/10.1093/gigascience/giac087eng2047-217Xhttps://academic.oup.com/gigascience/article/doi/10.1093/gigascience/giac087/6717722Preto, Antonio J.Matos-Filipe, PedroMourão, JoanaMoreira, Irina S.info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2022-12-02T11:12:56Zoai:estudogeral.uc.pt:10316/103273Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:20:08.068340Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv SYNPRED: prediction of drug combination effects in cancer using different synergy metrics and ensemble learning
title SYNPRED: prediction of drug combination effects in cancer using different synergy metrics and ensemble learning
spellingShingle SYNPRED: prediction of drug combination effects in cancer using different synergy metrics and ensemble learning
Preto, Antonio J.
biophysics
cancer
drug synergy
ensemble learning
interpretability
omics
Artificial Intelligence
Benchmarking
Drug Combinations
Humans
Machine Learning
Antineoplastic Agents
Neoplasms
title_short SYNPRED: prediction of drug combination effects in cancer using different synergy metrics and ensemble learning
title_full SYNPRED: prediction of drug combination effects in cancer using different synergy metrics and ensemble learning
title_fullStr SYNPRED: prediction of drug combination effects in cancer using different synergy metrics and ensemble learning
title_full_unstemmed SYNPRED: prediction of drug combination effects in cancer using different synergy metrics and ensemble learning
title_sort SYNPRED: prediction of drug combination effects in cancer using different synergy metrics and ensemble learning
author Preto, Antonio J.
author_facet Preto, Antonio J.
Matos-Filipe, Pedro
Mourão, Joana
Moreira, Irina S.
author_role author
author2 Matos-Filipe, Pedro
Mourão, Joana
Moreira, Irina S.
author2_role author
author
author
dc.contributor.author.fl_str_mv Preto, Antonio J.
Matos-Filipe, Pedro
Mourão, Joana
Moreira, Irina S.
dc.subject.por.fl_str_mv biophysics
cancer
drug synergy
ensemble learning
interpretability
omics
Artificial Intelligence
Benchmarking
Drug Combinations
Humans
Machine Learning
Antineoplastic Agents
Neoplasms
topic biophysics
cancer
drug synergy
ensemble learning
interpretability
omics
Artificial Intelligence
Benchmarking
Drug Combinations
Humans
Machine Learning
Antineoplastic Agents
Neoplasms
description In cancer research, high-throughput screening technologies produce large amounts of multiomics data from different populations and cell types. However, analysis of such data encounters difficulties due to disease heterogeneity, further exacerbated by human biological complexity and genomic variability. The specific profile of cancer as a disease (or, more realistically, a set of diseases) urges the development of approaches that maximize the effect while minimizing the dosage of drugs. Now is the time to redefine the approach to drug discovery, bringing an artificial intelligence (AI)-powered informational view that integrates the relevant scientific fields and explores new territories.
publishDate 2022
dc.date.none.fl_str_mv 2022
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10316/103273
http://hdl.handle.net/10316/103273
https://doi.org/10.1093/gigascience/giac087
url http://hdl.handle.net/10316/103273
https://doi.org/10.1093/gigascience/giac087
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2047-217X
https://academic.oup.com/gigascience/article/doi/10.1093/gigascience/giac087/6717722
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Oxford University Press
publisher.none.fl_str_mv Oxford University Press
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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