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Graduate in Mathematics (2010) and Computer Science (2010) at Universidad Autónoma de Madrid. MsC on Mathematics and applications at Universidad Autónoma de Madrid (2011). PhD in Mathematics (2015) at Universidad de Castilla-La Mancha. His research interests are discrete mathematics, machine learning, image processing and their applications to Mathematical Biology. |
Publications
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Approaching the Rank Aggregation Problem by Local Search-based Metaheuristics.
Journal of Computational and Applied Mathematics 354, 445-456 (2019)
J.A. Aledo, J.A. Gámez, D. Molina. -
Morphologic features on MR imaging classify multifocal glioblastomas in different prognostic groups
American Journal of Neuro-radiology 40 (4) 634-640 (2019).
J. Pérez-Beteta, D. Molina, M. Villena, M. Rodríguez, C. Velásquez, J. Martino, B. Meléndez, A.R. de Lope, R. Morcillo, J. Sepúlveda, A. Hernández, A. Ramos, J. Barcia, P. Lara, D. Albillo, A. Revert, E. Arana, V.M. Pérez-Garcia -
Morphological MRI-based features provide pretreatment and post-surgery survival prediction in glioblastoma
European Radiology 29(4) 1968-1977 (2019)
J. Pérez-Beteta, D. Molina-García, A. Martínez-González, M. Amo, A. Henares-Molina, B. Luque, E. Arregui, M. Calvo, J.M.Borrás, J. Martino, C. Velasquez, B. Meléndez, A.R. de Lope, R. Moreno, J.A. Barcia, B. Asenjo, M. Benavides, I. Herruzo, P.C. Lara, R. Cabrera, D. Albillo, M. Navarro, L.A. Pérez-Romasanta, A. Revert, E. Arana, V.M. Pérez-García -
Prognostic models based on imaging findings in glioblastoma: Human versus Machine
Scientific Reports 9:5982 (2019)
D. Molina-García, L. Vera, J. Pérez-Beteta, E. Arana, V.M. Pérez-García
Projects
- Establishing a non-invasive approach to accurately diagnose and assess brain tumors
Canadian Cancer Society (2024 - 2027) - Improving New Therapies in Oncology and Related Fields using mathematical models and biomedical data
Ministry of Science and Innovation (National Research Plan) (2023 - 2026) - Mathematical models for the digital transition in neuro-oncology: In-silico design of a clinical trial for glioblastoma.
Ministerio de Ciencia e Innovación (NextGenerationEU) (2023 - 2024) - Validation and transfer of oncological data and biomarkers based on mathematical models (AFTERbioMATH)
Ministry of Science and Innovation (Spain), Proof of Concept Program (2023 - 2024) - Mathematical models in Oncology
Ministerio de Ciencia e Innovación (Spain) (2020 - 2023)
Molina García, David
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Graduate in Mathematics (2010) and Computer Science (2010) at Universidad Autónoma de Madrid. MsC on Mathematics and applications at Universidad Autónoma de Madrid (2011). PhD in Mathematics (2015) at Universidad de Castilla-La Mancha. His research interests are discrete mathematics, machine learning, image processing and their applications to Mathematical Biology. |
Publications
- Approaching the Rank Aggregation Problem by Local Search-based Metaheuristics.
Journal of Computational and Applied Mathematics 354, 445-456 (2019)
J.A. Aledo, J.A. Gámez, D. Molina. - Morphologic features on MR imaging classify multifocal glioblastomas in different prognostic groups
American Journal of Neuro-radiology 40 (4) 634-640 (2019).
J. Pérez-Beteta, D. Molina, M. Villena, M. Rodríguez, C. Velásquez, J. Martino, B. Meléndez, A.R. de Lope, R. Morcillo, J. Sepúlveda, A. Hernández, A. Ramos, J. Barcia, P. Lara, D. Albillo, A. Revert, E. Arana, V.M. Pérez-Garcia - Morphological MRI-based features provide pretreatment and post-surgery survival prediction in glioblastoma
European Radiology 29(4) 1968-1977 (2019)
J. Pérez-Beteta, D. Molina-García, A. Martínez-González, M. Amo, A. Henares-Molina, B. Luque, E. Arregui, M. Calvo, J.M.Borrás, J. Martino, C. Velasquez, B. Meléndez, A.R. de Lope, R. Moreno, J.A. Barcia, B. Asenjo, M. Benavides, I. Herruzo, P.C. Lara, R. Cabrera, D. Albillo, M. Navarro, L.A. Pérez-Romasanta, A. Revert, E. Arana, V.M. Pérez-García - Prognostic models based on imaging findings in glioblastoma: Human versus Machine
Scientific Reports 9:5982 (2019)
D. Molina-García, L. Vera, J. Pérez-Beteta, E. Arana, V.M. Pérez-García
Projects
- Establishing a non-invasive approach to accurately diagnose and assess brain tumors
Canadian Cancer Society (2024 - 2027) - Improving New Therapies in Oncology and Related Fields using mathematical models and biomedical data
Ministry of Science and Innovation (National Research Plan) (2023 - 2026) - Mathematical models for the digital transition in neuro-oncology: In-silico design of a clinical trial for glioblastoma.
Ministerio de Ciencia e Innovación (NextGenerationEU) (2023 - 2024) - Validation and transfer of oncological data and biomarkers based on mathematical models (AFTERbioMATH)
Ministry of Science and Innovation (Spain), Proof of Concept Program (2023 - 2024) - Mathematical models in Oncology
Ministerio de Ciencia e Innovación (Spain) (2020 - 2023)
