Gilberto Pérez
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Gilberto Pérez has a total of 18 co-authors. Here are the top co-authors by number of shared publications:
- Abdul W. Basit
UCL School of Pharmacy, University College London
4 shared publications - Alvaro Goyanes
Departamento de Farmacología, Farmacia y Tecnología Farmacéutica, I+D Farma (GI-1645), Facultad de Farmacia, Materials Institute (iMATUS) and Health Research Institute of Santiago de Compostela (IDIS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
4 shared publications Brais Muñiz Castro
IRLab, CITIC Research Center, Department of Computer Science, University of A Coruña, Spain
4 shared publicationsJun Jie Ong
Department of Pharmaceutics, UCL School of Pharmacy, University College London, 29-39 Brunswick Square, London WC1N 1AX, UK
4 shared publicationsPedro Cabalar
IRLab, Department of Computer Science, University of A Coruña, Spain
4 shared publicationsMoe Elbadawi
Department of Pharmaceutics, UCL School of Pharmacy, University College London, 29-39 Brunswick Square, London WC1N 1AX, UK
2 shared publications
Latest Publications
- Open Access
- 17 April 2023
- Published by Elsevier in International Journal of Pharmaceutics: X
- Vol. 5, 100181
Predicting pharmaceutical inkjet printing outcomes using machine learning
- 17 April 2023
- Published by Elsevier in International Journal of Pharmaceutics: X
- Vol. 5, 100181
Open AccessPredicting pharmaceutical inkjet printing outcomes using machine learning
- Open Access
- 9 June 2022
- Published by Elsevier in International Journal of Pharmaceutics: X
- Vol. 4, 100120
Accelerating 3D printing of pharmaceutical products using machine learning
- 9 June 2022
- Published by Elsevier in International Journal of Pharmaceutics: X
- Vol. 4, 100120
Open AccessAccelerating 3D printing of pharmaceutical products using machine learning
- 1 September 2021
- Published by Elsevier in Journal of Controlled Release
- Vol. 337, 530-545
Machine learning predicts 3D printing performance of over 900 drug delivery systems
- 1 September 2021
- Published by Elsevier in Journal of Controlled Release
- Vol. 337, 530-545
Machine learning predicts 3D printing performance of over 900 drug delivery systems
- 1 November 2020
- Published by Elsevier in International Journal of Pharmaceutics
- Vol. 590, 119837
M3DISEEN: A novel machine learning approach for predicting the 3D printability of medicines
- 1 November 2020
- Published by Elsevier in International Journal of Pharmaceutics
- Vol. 590, 119837
M3DISEEN: A novel machine learning approach for predicting the 3D printability of medicines
- Open Access
- 28 August 2020
- Published by MDPI AG in Proceedings
- Vol. 54 (1) , 51
aspBEEF: Explaining Predictions Through Optimal Clustering
In this paper we introduce aspBEEF, a tool for generating explanations for the outcome of an arbitrary machine learning classifier. This is done using Grover’s et al. framework known as Balanced English Explanations of Forecasts (BEEF) that generates explanations in terms of in terms of finite intervals over the values of the input features. Since the problem of obtaining an optimal BEEF explanation has been proved to be NP-complete, BEEF existing implementation computes an approximation. In this work we use instead an encoding into the Answer Set Programming paradigm, specialized in solving NP problems, to guarantee that the computed solutions are optimal.
- 28 August 2020
- Published by MDPI AG in Proceedings
- Vol. 54 (1) , 51
Open AccessaspBEEF: Explaining Predictions Through Optimal Clustering
In this paper we introduce aspBEEF, a tool for generating explanations for the outcome of an arbitrary machine learning classifier. This is done using Grover’s et al. framework known as Balanced English Explanations of Forecasts (BEEF) that generates explanations in terms of in terms of finite intervals over the values of the input features. Since the problem of obtaining an optimal BEEF explanation has been proved to be NP-complete, BEEF existing implementation computes an approximation. In this work we use instead an encoding into the Answer Set Programming paradigm, specialized in solving NP problems, to guarantee that the computed solutions are optimal.