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Uncertainty Assessment of Proarrhythmia Predictions Derived from Multi-Level in Silico Models

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Uncertainty Assessment of Proarrhythmia Predictions Derived from Multi-Level in Silico Models

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[Excel]
Nombre: PopulationDrugsTe ...
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Formato: Microsoft Excel 2007
Descripción: APD90s (ms) distr ...
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dc.contributor.author Kopanska, Karolina es_ES
dc.contributor.author Rodríguez-Belenguer, Pablo es_ES
dc.contributor.author Llopis-Lorente, Jordi es_ES
dc.contributor.author Trénor, Beatriz es_ES
dc.contributor.author Saiz, Javier es_ES
dc.contributor.author Pastor, Manuel es_ES
dc.date.accessioned 2023-02-14T09:37:59Z
dc.date.available 2023-02-14T09:37:59Z
dc.date.issued 2023-02-14T09:37:59Z
dc.identifier.uri http://hdl.handle.net/10251/191820
dc.description.abstract [EN] These datasets were generated to train ("PopulationDrugsTrainingKrNaLCaL.xlsx") and test ("PopulationDrugsTestKrCaLNaL.xlsx") the uncertainty assessment models developed in the paper "Uncertainty assessment of proarrhythmia predictions derived from multi-level in silico models" by Kopanska, Rodríguez-Belenguer, et al.
dc.description.sponsorship The authors received funding from the eTRANSAFE project, Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 777365, supported from European Union's Horizon 2020 and the EFPIA. We also received funding from the SimCardioTest supported by European Union’s Horizon 2020 research and innovation programme under grant agreement No 101016496. J.L.L. is being funded by the Ministerio de Ciencia, Innovacion y Universidades for the “Formacion de Profesorado Universitario” (Grant Reference: FPU18/01659). The work was also partially support by the Dirección General de Política Científica de la Generalitat Valenciana (PROMETEO/ 2020/043). es_ES
dc.language Inglés es_ES
dc.publisher Universitat Politècnica de València es_ES
dc.relation.uri https://riunet.upv.es/handle/10251/205317
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject In silico es_ES
dc.subject Uncertainty quantification es_ES
dc.subject Cardiac safety es_ES
dc.subject Proarrhythmic risk es_ES
dc.subject Torsade de Pointes es_ES
dc.title Uncertainty Assessment of Proarrhythmia Predictions Derived from Multi-Level in Silico Models es_ES
dc.type Dataset es_ES
dc.identifier.doi 10.4995/Dataset/10251/191820 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/GVA//PROMETEO%2F2020%2F043//MODELOS IN-SILICO MULTI-FISICOS Y MULTI-ESCALA DEL CORAZON PARA EL DESARROLLO DE NUEVOS METODOS DE PREVENCION, DIAGNOSTICO Y TRATAMIENTO EN MEDICINA PERSONALIZADA (HEART IN-SILICO MODELS)/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MICIU//FPU18%2F01659 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/101016496/EU/Simulation of Cardiac Devices & Drugs for in-silico Testing and Certification/SimCardioTest es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/777365/EU/Enhacing TRANslational SAFEty Assessment through Integrative Knowledge Management/eTRANSAFE es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Centro de Investigación e Innovación en Bioingeniería - Centre de Recerca i Innovació en Bioenginyeria es_ES
dc.description.bibliographicCitation Kopanska, K.; Rodríguez-Belenguer, P.; Llopis-Lorente, J.; Trénor, B.; Saiz, J.; Pastor, M. (2023). Uncertainty Assessment of Proarrhythmia Predictions Derived from Multi-Level in Silico Models. Universitat Politècnica de València. https://doi.org/10.4995/Dataset/10251/191820 es_ES
dc.type.version info:eu-repo/semantics/submittedVersion es_ES
dc.contributor.funder European Commission es_ES
dc.contributor.funder Ministerio de Ciencia, Innovación y Universidades


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