David Molina

452 total citations
17 papers, 315 citations indexed

About

David Molina is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Genetics. According to data from OpenAlex, David Molina has authored 17 papers receiving a total of 315 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Radiology, Nuclear Medicine and Imaging, 5 papers in Artificial Intelligence and 4 papers in Genetics. Recurrent topics in David Molina's work include Radiomics and Machine Learning in Medical Imaging (7 papers), Glioma Diagnosis and Treatment (4 papers) and Multi-Criteria Decision Making (4 papers). David Molina is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (7 papers), Glioma Diagnosis and Treatment (4 papers) and Multi-Criteria Decision Making (4 papers). David Molina collaborates with scholars based in Spain, Cuba and United States. David Molina's co-authors include Juan A. Aledo, José A. Gámez, Julián Pérez-Beteta, Alicia Martínez‐González, Victor M. Pérez-Garcı́a, Juan Martino, Carlos Velásquez, Estanislao Arana, Ismael Herruzo and Carlos López and has published in prestigious journals such as Journal of Clinical Oncology, PLoS ONE and Applied Soft Computing.

In The Last Decade

David Molina

17 papers receiving 311 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
David Molina Spain 10 164 70 69 49 46 17 315
Michael J. Fox United States 9 162 1.0× 82 1.2× 29 0.4× 27 0.6× 57 1.2× 25 386
Wentian Guo China 10 255 1.6× 105 1.5× 11 0.2× 36 0.7× 70 1.5× 15 516
Erdal Taşçı Türkiye 11 182 1.1× 197 2.8× 65 0.9× 40 0.8× 5 0.1× 39 532
Antonio Foncubierta–Rodríguez Switzerland 13 233 1.4× 159 2.3× 13 0.2× 72 1.5× 9 0.2× 29 532
Rui Fu China 11 42 0.3× 34 0.5× 25 0.4× 23 0.5× 28 0.6× 43 432
Edward Kien Yee Yapp Singapore 9 69 0.4× 81 1.2× 28 0.4× 21 0.4× 4 0.1× 10 585
Ilya Levner Canada 10 66 0.4× 125 1.8× 38 0.6× 18 0.4× 16 0.3× 23 454
Danqing Zhang United States 13 84 0.5× 110 1.6× 43 0.6× 29 0.6× 8 0.2× 28 418
Jun Tan United States 12 231 1.4× 88 1.3× 13 0.2× 97 2.0× 48 1.0× 50 601
Zhiqiong Wang China 14 64 0.4× 35 0.5× 27 0.4× 24 0.5× 32 0.7× 68 483

Countries citing papers authored by David Molina

Since Specialization
Citations

This map shows the geographic impact of David Molina's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by David Molina with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Molina more than expected).

Fields of papers citing papers by David Molina

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David Molina. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by David Molina. The network helps show where David Molina may publish in the future.

Co-authorship network of co-authors of David Molina

This figure shows the co-authorship network connecting the top 25 collaborators of David Molina. A scholar is included among the top collaborators of David Molina based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with David Molina. David Molina is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

17 of 17 papers shown
1.
Muro, Xavier García del, Patrizia Giannatempo, Daniel Castellano, et al.. (2023). Phase II study of the efficacy of retifanlimab (Rf) (INCMGA00012) in penile squamous cell carcinoma (PSqCC): ORPHEUS final analysis.. Journal of Clinical Oncology. 41(16_suppl). 5043–5043. 3 indexed citations
2.
Aledo, Juan A., José A. Gámez, & David Molina. (2018). Approaching the rank aggregation problem by local search-based metaheuristics. Journal of Computational and Applied Mathematics. 354. 445–456. 9 indexed citations
3.
Aledo, Juan A., José A. Gámez, David Molina, & Alejandro Rosete Suárez. (2018). Consensus‐based journal rankings: A complementary tool for bibliometric evaluation. Journal of the Association for Information Science and Technology. 69(7). 936–948. 9 indexed citations
4.
Molina, David, Julián Pérez-Beteta, Alicia Martínez‐González, et al.. (2017). Lack of robustness of textural measures obtained from 3D brain tumor MRIs impose a need for standardization. PLoS ONE. 12(6). e0178843–e0178843. 50 indexed citations
5.
Pérez-Beteta, Julián, et al.. (2017). Papel predictivo y pronóstico de las variables volumétricas metabólicas obtenidas en la 18 F-FDG PET/TC en el cáncer de mama con indicación de quimioterapia neoadyuvante. Revista Española de Medicina Nuclear e Imagen Molecular. 37(2). 73–79. 10 indexed citations
6.
Molina, David, et al.. (2017). Textural features and SUV-based variables assessed by dual time point 18F-FDG PET/CT in locally advanced breast cancer. Annals of Nuclear Medicine. 31(10). 726–735. 17 indexed citations
7.
Pérez-Beteta, Julián, David Molina, Alicia Martínez‐González, et al.. (2017). P09.43 Novel geometrical imaging biomarkers predict survival and allow for patient selection for surgery in glioblastoma patients.. Neuro-Oncology. 19(suppl_3). iii80–iii80. 1 indexed citations
8.
Molina, David, Julián Pérez-Beteta, Alicia Martínez‐González, et al.. (2016). Geometrical Measures Obtained from Pretreatment Postcontrast T1 Weighted MRIs Predict Survival Benefits from Bevacizumab in Glioblastoma Patients. PLoS ONE. 11(8). e0161484–e0161484. 10 indexed citations
9.
Aledo, Juan A., et al.. (2016). FSS-OBOP: Feature subset selection guided by a bucket order consensus ranking. 2. 1–8. 1 indexed citations
10.
Pérez-Beteta, Julián, et al.. (2016). Metabolic Tumor Burden Assessed by Dual Time Point [18F]FDG PET/CT in Locally Advanced Breast Cancer: Relation with Tumor Biology. Molecular Imaging and Biology. 19(4). 636–644. 8 indexed citations
11.
Molina, David, Julián Pérez-Beteta, Carlos López, et al.. (2016). Tumour heterogeneity in glioblastoma assessed by MRI texture analysis: a potential marker of survival. British Journal of Radiology. 89(1064). 20160242–20160242. 51 indexed citations
12.
Aledo, Juan A., José A. Gámez, & David Molina. (2016). Using extension sets to aggregate partial rankings in a flexible setting. Applied Mathematics and Computation. 290. 208–223. 22 indexed citations
13.
Molina, David, Julián Pérez-Beteta, Alicia Martínez‐González, et al.. (2016). Influence of gray level and space discretization on brain tumor heterogeneity measures obtained from magnetic resonance images. Computers in Biology and Medicine. 78. 49–57. 49 indexed citations
14.
Aledo, Juan A., José A. Gámez, & David Molina. (2016). Tackling the supervised label ranking problem by bagging weak learners. Information Fusion. 35. 38–50. 28 indexed citations
15.
Aledo, Juan A., José A. Gámez, & David Molina. (2015). Using metaheuristic algorithms for parameter estimation in generalized Mallows models. Applied Soft Computing. 38. 308–320. 11 indexed citations
16.
Sánchez, Íñigo, et al.. (2015). Primera cita de Trachymela sloanei (Blackburn, 1897) (Coleoptera, Chrysomelidae) en Europa.. Zenodo (CERN European Organization for Nuclear Research). 6(1). 127–130. 3 indexed citations
17.
Aledo, Juan A., José A. Gámez, & David Molina. (2013). Tackling the rank aggregation problem with evolutionary algorithms. Applied Mathematics and Computation. 222. 632–644. 33 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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