Machine learning prediction of side effects for drugs in clinical trials
Correspondence: dgaleano@ing.una.py.
-д хадгалсан:
| Үндсэн зохиолч: | |
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| Бусад зохиолчид: | |
| Формат: | article |
| Хэл сонгох: | англи |
| Хэвлэсэн: |
2022
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| Нөхцлүүд: | |
| Онлайн хандалт: | https://doi.org/10.1016/j.crmeth.2022.100358 http://hdl.handle.net/20.500.14066/4732 |
| Шошгууд: |
Шошго байхгүй, Энэхүү баримтыг шошголох эхний хүн болох!
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| _version_ | 1870612065956134912 |
|---|---|
| author | Galeano Galeano, Diego Ariel |
| author2 | Paccanaro, Alberto |
| author2_role | author |
| author_browse | Galeano Galeano, Diego Ariel Paccanaro, Alberto |
| author_facet | Galeano Galeano, Diego Ariel Paccanaro, Alberto |
| author_role | author |
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| bitstream.url.fl_str_mv | http://repositorio.conacyt.gov.py/bitstream/20.500.14066/4732/1/Machine%20learning%20prediction%20of%20side%20effects%20for%20drugs%20in%20clinical%20trials.pdf http://repositorio.conacyt.gov.py/bitstream/20.500.14066/4732/6/mmc1.pdf http://repositorio.conacyt.gov.py/bitstream/20.500.14066/4732/7/mmc2.pdf http://repositorio.conacyt.gov.py/bitstream/20.500.14066/4732/2/license_rdf http://repositorio.conacyt.gov.py/bitstream/20.500.14066/4732/3/license.txt http://repositorio.conacyt.gov.py/bitstream/20.500.14066/4732/4/Machine%20learning%20prediction%20of%20side%20effects%20for%20drugs%20in%20clinical%20trials.pdf.txt http://repositorio.conacyt.gov.py/bitstream/20.500.14066/4732/8/mmc1.pdf.txt http://repositorio.conacyt.gov.py/bitstream/20.500.14066/4732/9/mmc2.pdf.txt http://repositorio.conacyt.gov.py/bitstream/20.500.14066/4732/5/Machine%20learning%20prediction%20of%20side%20effects%20for%20drugs%20in%20clinical%20trials.jpg |
| dc.contributor.other.es.fl_str_mv | Universidad Católica Nuestra Señora de la Asunción Cámara Paraguaya de Exportadores y Comercializadores de Cereales y Oleaginosas Centro de Ingeniería para la Investigación, Desarrollo e Innovación Tecnológica |
| dc.creator.none.fl_str_mv | Galeano Galeano, Diego Ariel Paccanaro, Alberto |
| dc.date.accessioned.none.fl_str_mv | 2026-01-06T15:20:00Z |
| dc.date.available.none.fl_str_mv | 2026-01-06T15:20:00Z |
| dc.date.issued.none.fl_str_mv | 2022-12-07 |
| dc.format.extent.es.fl_str_mv | 18 páginas |
| dc.identifier.citation.en.fl_str_mv | Galeano, D., & Paccanaro, A. (2022). Machine learning prediction of side effects for drugs in clinical trials. Cell Reports Methods, 2(12), Artículo 100358. https://doi.org/10.1016/j.crmeth.2022.100358 |
| dc.identifier.doi.es.fl_str_mv | 10.1016/j.crmeth.2022.100358 |
| dc.identifier.essn.es.fl_str_mv | 2667-2375 |
| dc.identifier.other.es.fl_str_mv | https://doi.org/10.1016/j.crmeth.2022.100358 |
| dc.identifier.uri.none.fl_str_mv | http://hdl.handle.net/20.500.14066/4732 |
| dc.language.iso.es.fl_str_mv | eng |
| dc.publisher.es.fl_str_mv | Cell Press |
| dc.relation.projectCONACYT.es.fl_str_mv | 14-INV-088 PINV15-315 PINV20-337 |
| dc.rights.*.fl_str_mv | Atribución/Reconocimiento 4.0 Internacional |
| dc.rights.accessRights.es.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.rights.copyright.es.fl_str_mv | © 2022 The Authors |
| dc.rights.uri.*.fl_str_mv | http://creativecommons.org/licenses/by/4.0/ |
| dc.subject.classification.en.fl_str_mv | 6.16. Manufacture of basic pharmaceutical products and pharmaceutical preparations |
| dc.subject.classification.es.fl_str_mv | 6. Producción y tecnología industrial 8. Agricultura 8.2. Fertilizantes químicos, biocidas, control biológico de plagas y mecanización de la agricultura |
| dc.subject.ocde.es.fl_str_mv | 4. Ciencias Agrícolas y Veterinarias 4.1. Agricultura, silvicultura, pesca y ciencias afines (agronomía, zootecnia, pesca, silvicultura, horticultura, otras disciplinas afines) |
| dc.subject.other.es.fl_str_mv | Adverse drug effect Adverse drug events Clinical trials Computational modeling Computational pharmacology Drug side effect prediction Interpretable model Machine learning Matrix completion Networks |
| dc.title.es.fl_str_mv | Machine learning prediction of side effects for drugs in clinical trials |
| dc.type.es.fl_str_mv | info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
| description | Correspondence: dgaleano@ing.una.py. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | CONACYT_23bb42a683627fca72b1dd15c6a7c8b9 |
| identifier_str_mv | Galeano, D., & Paccanaro, A. (2022). Machine learning prediction of side effects for drugs in clinical trials. Cell Reports Methods, 2(12), Artículo 100358. https://doi.org/10.1016/j.crmeth.2022.100358 10.1016/j.crmeth.2022.100358 2667-2375 |
| language | eng |
| network_acronym_str | CONACYT |
| network_name_str | Repositorio Institucional CONACYT |
| oai_identifier_str | oai:repositorio.conacyt.gov.py:20.500.14066/4732 |
| publishDate | 2022 |
| publishDateSort | 2022 |
| repository.mail.fl_str_mv | repositorio.institucional@conacyt.gov.py |
| repository.name.fl_str_mv | Repositorio Institucional CONACYT |
| repository_id_str | |
| rights_invalid_str_mv | Atribución/Reconocimiento 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ © 2022 The Authors |
| spelling | 47460024e1bdd6-b0df-48fb-a39e-baec166f788d600Universidad Católica Nuestra Señora de la AsunciónCámara Paraguaya de Exportadores y Comercializadores de Cereales y OleaginosasCentro de Ingeniería para la Investigación, Desarrollo e Innovación Tecnológica2026-01-06T15:20:00Z2026-01-06T15:20:00Z2022-12-07Galeano, D., & Paccanaro, A. (2022). Machine learning prediction of side effects for drugs in clinical trials. Cell Reports Methods, 2(12), Artículo 100358. https://doi.org/10.1016/j.crmeth.2022.100358https://doi.org/10.1016/j.crmeth.2022.100358http://hdl.handle.net/20.500.14066/473210.1016/j.crmeth.2022.1003582667-2375Correspondence: dgaleano@ing.una.py.Early and accurate detection of side effects is critical for the clinical success of drugs under development. Here, we aim to predict unknown side effects for drugs with a small number of side effects identified in randomized controlled clinical trials. Our machine learning framework, the geometric self-expressive model (GSEM), learns globally optimal self-representations for drugs and side effects from pharmacological graph networks. We show the usefulness of the GSEM on 505 therapeutically diverse drugs and 904 side effects from multiple human physiological systems. Here, we also show a data integration strategy that could be adopted to improve the ability of side effect prediction models to identify unknown side effects that might only appear after the drug enters the market.Consejo Nacional de Ciencia y TecnologíaPrograma Paraguayo para el Desarrollo de la Ciencia y Tecnología. Proyectos de investigación y desarrollo18 páginasengCell PressAtribución/Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccess© 2022 The Authors6. Producción y tecnología industrial8. Agricultura8.2. Fertilizantes químicos, biocidas, control biológico de plagas y mecanización de la agricultura6.16. Manufacture of basic pharmaceutical products and pharmaceutical preparationsAdverse drug effectAdverse drug eventsClinical trialsComputational modelingComputational pharmacologyDrug side effect predictionInterpretable modelMachine learningMatrix completionNetworks4. Ciencias Agrícolas y Veterinarias4.1. Agricultura, silvicultura, pesca y ciencias afines (agronomía, zootecnia, pesca, silvicultura, horticultura, otras disciplinas afines)Machine learning prediction of side effects for drugs in clinical trialsinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersion12Cell Reports Methods14-INV-088PINV15-315PINV20-3372Galeano Galeano, Diego ArielPaccanaro, AlbertoORIGINALMachine learning prediction of side effects for drugs in clinical trials.pdfMachine learning prediction of side effects for drugs in clinical trials.pdfArtículo científicoapplication/pdf2963477http://repositorio.conacyt.gov.py/bitstream/20.500.14066/4732/1/Machine%20learning%20prediction%20of%20side%20effects%20for%20drugs%20in%20clinical%20trials.pdfd59af030f33f3c488e5f1c2adb1da2ccMD51mmc1.pdfmmc1.pdfInformación complementaria. 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| spellingShingle | Machine learning prediction of side effects for drugs in clinical trials Galeano Galeano, Diego Ariel 6. Producción y tecnología industrial 8. Agricultura 8.2. Fertilizantes químicos, biocidas, control biológico de plagas y mecanización de la agricultura 6.16. Manufacture of basic pharmaceutical products and pharmaceutical preparations Adverse drug effect Adverse drug events Clinical trials Computational modeling Computational pharmacology Drug side effect prediction Interpretable model Machine learning Matrix completion Networks 4. Ciencias Agrícolas y Veterinarias 4.1. Agricultura, silvicultura, pesca y ciencias afines (agronomía, zootecnia, pesca, silvicultura, horticultura, otras disciplinas afines) |
| status_str | publishedVersion |
| title | Machine learning prediction of side effects for drugs in clinical trials |
| title_full | Machine learning prediction of side effects for drugs in clinical trials |
| title_fullStr | Machine learning prediction of side effects for drugs in clinical trials |
| title_full_unstemmed | Machine learning prediction of side effects for drugs in clinical trials |
| title_short | Machine learning prediction of side effects for drugs in clinical trials |
| title_sort | Machine learning prediction of side effects for drugs in clinical trials |
| topic | 6. Producción y tecnología industrial 8. Agricultura 8.2. Fertilizantes químicos, biocidas, control biológico de plagas y mecanización de la agricultura 6.16. Manufacture of basic pharmaceutical products and pharmaceutical preparations Adverse drug effect Adverse drug events Clinical trials Computational modeling Computational pharmacology Drug side effect prediction Interpretable model Machine learning Matrix completion Networks 4. Ciencias Agrícolas y Veterinarias 4.1. Agricultura, silvicultura, pesca y ciencias afines (agronomía, zootecnia, pesca, silvicultura, horticultura, otras disciplinas afines) |
| url | https://doi.org/10.1016/j.crmeth.2022.100358 http://hdl.handle.net/20.500.14066/4732 |