Software
URI permanente para esta colección
Software creado por personal de la Universitat Politècnica de València que permiten su acceso y difusión con la finalidad de incrementar su visibilidad y garantizar su accesibilidad y preservación.
Examinar
Envíos recientes
- PublicaciónTexMiLAB(2024) Periñán Pascual, José Carlos; Departamento de Lingüística Aplicada; Escuela Politécnica Superior de Gandia; Grupo de Análisis de las Lenguas de Especialidad (GALE); Agencia Estatal de Investigación[EN] TexMiLAB is a workbench that allows researchers to do text-mining experiments in two modes: graphical user interface and C# scripting interface.
- PublicaciónClass3Dp(2023-06-01T09:32:35Z) Carbonell Rivera, Juan Pedro; Ruiz Fernández, Luis Ángel; Estornell Cremades, Javier; Departamento de Ingeniería Cartográfica Geodesia y Fotogrametría; Escuela Técnica Superior de Ingeniería Geodésica, Cartográfica y Topográfica; Escuela Politécnica Superior de Gandia; Grupo de Cartografía Geoambiental y Teledetección; Agencia Estatal de Investigación; Ministerio de Economía y Competitividad[EN] Class3Dp is a software tool developed for supervised classification of photogrammetric point clouds. It allows the selection of training samples and classification of point clouds according to different features and machine learning models. Point clouds obtained from photogrammetric algorithms (e.g., Structure from Motion) include spectral information recorded in the image acquisition process using RGB, multispectral or hyperspectral sensors. The software extracts spectral and geometric features from the point clouds to be used for point classification.
- PublicaciónApplication of Machine Learning to improve the efficiency of electrophysiological simulations used for the prediction of drug-induced ventricular arrhythmia(Universitat Politècnica de València, 2022-06-03T09:56:55Z) Rodríguez-Belenguer, Pablo; Kopańska, Karolina; Llopis Lorente, Jordi; Trénor Gomis, Beatriz Ana; Saiz Rodríguez, Francisco Javier; Pastor, Manuel; Departamento de Ingeniería Electrónica; Escuela Técnica Superior de Ingeniería Aeroespacial y Diseño Industrial; Escuela Técnica Superior de Ingeniería Industrial; Centro de Investigación e Innovación en Bioingeniería; Agencia Estatal de Investigación; Generalitat Valenciana; European Commission; Innovative Medicines Initiative 2 Joint Undertaking (IMI2/IU) N. 777365 (eTRANSAFE)In cardiotoxicity studies it is common to pre-compute the values of different biomarkers (my equation or TX) for a range of ion channel blockades. Since every simulation requires costly computations, to complete the matrix of simulations for several ion channels can be cumbersome. Some examples of how these simulations are run and used are included in the references. The relationship between the input values and the biomarker is not too complex and Machine Learning can be used to obtain a good approximation. The resulting function can be generated using only an small fraction of the computations required to generate the whole matrix. This function can then be used to predict the biomarker value for any combination of the covered range, with an excellent accuracy In this repository we have included a jupyter notebook and some simulation results that demonstrate this idea. Regarding the data matrices, they correspond to simulations using a modified version of the ventricular action potential model by O'Hara et al., which have been performed by Jordi Llopis, Beatriz Trenor and Javier Saiz at the Centro de Investigación e Innovación en Bioingeniería (Ci2B), Universitat Politècnica de València, Valencia, Spain KrKsCaL.xlsx: This is the data matrix needed to build the ML models. APD90_12CiPA_drugs_IKrIKsICaL.xlsx: This excel file contains the input and output values for CiPA compounds. EFTPC_IC50_28_CiPADrugs.xlsx: This file contains D, my equation and hill coefficient to calculate the input values for CiPA compounds of the previous excel file. Folder "Matrix Building": This folder contains MATLAB functions for generating the KrKsCaL matrix. The script "buildMatrixKrKsNaL.m" is the main script which run the electrophysioloigcal simulations and generates the matrix References Llopis J, Cano J, Gomis-Tena J, Romero L, Sanz F, Pastor M, Trenor B, Saiz J. In silico assay for preclinical assessment of drug proarrhythmicity. J Pharmacol Toxicol Methods 2019 99: 106595. PMID: 31962986 DOI: 10.1016/j.vascn.2019.05.106. O’Hara, T., Virág, L., Varró, A. & Rudy, Y. Simulation of the Undiseased Human Cardiac Ventricular Action Potential: Model Formulation and Experimental Validation. PLOS Comput. Biol. 7, e1002061 (2011). Licensing CardioML was produced at the PharmacoInformatics lab (http://phi.upf.edu), in the framework of the eTRANSAFE project (http://etransafe.eu). eTRANSAFE has received support from IMI2 Joint Undertaking under Grant Agreement No. 777365. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and the European Federation of Pharmaceutical Industries and Associations (EFPIA). Copyright 2022 Manuel Pastor (manuel.pastor@upf.edu) CardioML is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation version 3. CardioML is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with CardioML source code. If not, see http://www.gnu.org/licenses/.
- PublicaciónConsidering Population Variability of Electrophysiological Models Improves the In Silico Assessment of Drug-Induced Torsadogenic Risk(2022-05-13T13:26:43Z) Llopis Lorente, Jordi; Trénor Gomis, Beatriz Ana; Saiz Rodríguez, Francisco Javier; Departamento de Ingeniería Electrónica; Escuela Técnica Superior de Ingeniería Aeroespacial y Diseño Industrial; Escuela Técnica Superior de Ingeniería Industrial; Centro de Investigación e Innovación en Bioingeniería; Universitat Politècnica de València; Generalitat Valenciana; European Commission; Ministerio de Ciencia, Innovación y UniversidadesThis repository contains the parameter sets of the population of TorORd models and the the population of ORdmD models, the ORdmD CellML file and MALTAB code used in Llopis-Lorente, J., Trenor, B., Saiz, J. (2022). Considering Population Variability of Electrophysiological Models Improves the In Silico Assessment of Drug-Induced Torsadogenic Risk
- PublicaciónSynthetic input data generator for a MILP model for lot-sizing and scheduling of automotive plastic components with availability of raw materials and packaging(2021-09-14T12:09:29Z) Guzmán Ortiz, Brunnel Eduardo; Andrés Navarro, Beatriz; Poler Escoto, Raúl; Departamento de Organización de Empresas; Centro de Investigación en Gestión e Ingeniería de Producción; Escuela Politécnica Superior de AlcoyThe Python code generates synthetic input data. The dataset contains the input data to develop the experiments for the mathematical model (mixed integer linear programming model for lot-sizing and scheduling of automotive plastic components with availability of raw materials and packaging)