Mostrar o rexistro simple do ítem
Prediction of Antimalarial Drug-Decorated Nanoparticle Delivery Systems with Random Forest Models
dc.contributor.author | Urista, Diana V. | |
dc.contributor.author | Carrué, Diego B. | |
dc.contributor.author | Otero, Iago | |
dc.contributor.author | Arrasate, Sonia | |
dc.contributor.author | Quevedo‐Tumailli, Viviana F. | |
dc.contributor.author | Gestal, M. | |
dc.contributor.author | González-Díaz, Humberto | |
dc.contributor.author | Munteanu, Cristian-Robert | |
dc.date.accessioned | 2024-06-28T14:38:42Z | |
dc.date.available | 2024-06-28T14:38:42Z | |
dc.date.issued | 2020-07 | |
dc.identifier.citation | Urista, D.V.; Carrué, D.B.; Otero, I.; Arrasate, S.; Quevedo-Tumailli, V.F.; Gestal, M.; González-Díaz, H.; Munteanu, C.R. Prediction of Antimalarial Drug-Decorated Nanoparticle Delivery Systems with Random Forest Models. Biology 2020, 9, 198. https://doi.org/10.3390/biology9080198 | es_ES |
dc.identifier.issn | 2079-7737 | |
dc.identifier.uri | http://hdl.handle.net/2183/37552 | |
dc.description.abstract | [Abstract]: Drug-decorated nanoparticles (DDNPs) have important medical applications. The current work combined Perturbation Theory with Machine Learning and Information Fusion (PTMLIF). Thus, PTMLIF models were proposed to predict the probability of nanoparticle–compound/drug complexes having antimalarial activity (against Plasmodium). The aim is to save experimental resources and time by using a virtual screening for DDNPs. The raw data was obtained by the fusion of experimental data for nanoparticles with compound chemical assays from the ChEMBL database. The inputs for the eight Machine Learning classifiers were transformed features of drugs/compounds and nanoparticles as perturbations of molecular descriptors in specific experimental conditions (experiment-centered features). The resulting dataset contains 107 input features and 249,992 examples. The best classification model was provided by Random Forest, with 27 selected features of drugs/compounds and nanoparticles in all experimental conditions considered. The high performance of the model was demonstrated by the mean Area Under the Receiver Operating Characteristics (AUC) in a test subset with a value of 0.9921 ± 0.000244 (10-fold cross-validation). The results demonstrated the power of information fusion of the experimental-centered features of drugs/compounds and nanoparticles for the prediction of nanoparticle–compound antimalarial activity. The scripts and dataset for this project are available in the open GitHub repository. | es_ES |
dc.description.sponsorship | This research and the APC were funded by Consolidation and Structuring of Competitive Research Units—Competitive Reference Groups (ED431C 2018/49) funded by the Ministry of Education, University and Vocational Training of Xunta de Galicia endowed with EU FEDER funds. This work was supported by the Collaborative Project in Genomic Data Integration (CICLOGEN)’’ PI17/01826 funded by the Carlos III Health Institute from the Spanish National plan for Scientific and Technical Research and Innovation 2013–2016 and the European Regional Development Funds (FEDER)—“A way to build Europe”. This project was also supported by the General Directorate of Culture, Education and University Management of Xunta de Galicia ED431D 2017/16 and “Drug Discovery Galician Network” Ref. ED431G/01 and the “Galician Network for Colorectal Cancer Research” (Ref. ED431D 2017/23), and finally by the Spanish Ministry of Economy and Competitiveness for its support through the funding of the unique installation BIOCAI (UNLC08-1E-002, UNLC13-13-3503) and the European Regional Development Funds (FEDER) by the European Union. Additional support was offered by the research grants from Ministry of Economy and Competitiveness, MINECO, Spain (FEDER CTQ2016-74881-P), Basque government (IT1045-16), and kind support of Ikerbasque, Basque Foundation for Science. | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2018/49 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G/01 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431D 2017/23 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431D 2017/16 | es_ES |
dc.description.sponsorship | Eusko Jaurlaritza = Gobierno Vasco; IT1045-16 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.relation | info:eu-repo/grantAgreement/ISCIII/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013–2016/PI17%2F01826/ES/PROYECTO COLABORATIVO DE INTEGRACION DE DATOS GENOMICOS (CICLOGEN). TECNICAS DE DATA MINING Y DOCKING MOLECULAR PARA ANALISIS DE DATOS INTEGRATIVOS EN CANCER DE COLON/ | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/Plan Nacional de I+D+I 2008-2011/UNLC08-1E-002/ES/INFRAESTRUCTURA COMPUTACIONAL PARA LA RED GALLEGA DE BIOINFORMÁTICA | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/UNLC13-1E-2503/ES/PLATAFORMA HPC-PLUS PARA APLICACIONES BIOMÉDICAS | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/CTQ2016-74881-P/ES/REACCIONES DE ACTIVACION C-H CATALIZADAS POR METALES DE TRANSICION EN SINTESIS Y FUNCIONALIZACION DE HETEROCICLOS. NUEVAS APLICACIONES SINTETICAS Y MODELOS COMPUTACIONALES/ | es_ES |
dc.relation.uri | https://doi.org/10.3390/biology9080198 | es_ES |
dc.rights | Atribución 4.0 Internacional | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | Decorated nanoparticles | es_ES |
dc.subject | Drug delivery | es_ES |
dc.subject | Antimalarial compounds | es_ES |
dc.subject | Big data | es_ES |
dc.subject | Perturbation Theory | es_ES |
dc.subject | Machine Learning | es_ES |
dc.subject | ChEMBL database | es_ES |
dc.title | Prediction of Antimalarial Drug-Decorated Nanoparticle Delivery Systems with Random Forest Models | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Biology | es_ES |
UDC.volume | 9 | es_ES |
UDC.issue | 198 | es_ES |
UDC.startPage | 1 | es_ES |
UDC.endPage | 15 | es_ES |
dc.identifier.doi | 10.3390/biology9080198 |
Ficheiros no ítem
Este ítem aparece na(s) seguinte(s) colección(s)
-
GI-RNASA - Artigos [193]