Javier Pérez-Rodríguez
- Artificial Intelligence top 5%
- Radiology, Nuclear Medicine and Imaging
- Materials Chemistry
- Computer Vision and Pattern Recognition top 10%
- Molecular Biology
- Co-authors
- Nicolás García‐PedrajasAida de Haro-GarcíaDerek M. FineDavid A. BluemkeBenjamin D. EhstMaría D. García‐PedrajasColin FyfeDomingo Ortíz-Boyer
- Topics
- Machine Learning and Data Classification (7 papers)Machine Learning in Bioinformatics (6 papers)Machine Learning and ELM (5 papers)
- Cited by
- Artificial IntelligenceRadiology, Nuclear Medicine and ImagingComputer Vision and Pattern Recognition
- Partner nations
- SpainUnited StatesUnited Kingdom
In The Last Decade
Javier Pérez-Rodríguez
23 papers receiving 504 citations
Peers
Comparison fields: 5 of 97
- Artificial Intelligence 258
- Radiology, Nuclear Medicine and Imaging 107
- Materials Chemistry 92
- Computer Vision and Pattern Recognition 79
- Molecular Biology 49
Countries citing papers authored by Javier Pérez-Rodríguez
This map shows the geographic impact of Javier Pérez-Rodríguez'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 Javier Pérez-Rodríguez with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Javier Pérez-Rodríguez more than expected).
Fields of papers citing papers by Javier Pérez-Rodríguez
This network shows the impact of papers produced by Javier Pérez-Rodríguez. 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 Javier Pérez-Rodríguez. The network helps show where Javier Pérez-Rodríguez may publish in the future.
Co-authorship network of co-authors of Javier Pérez-Rodríguez
This figure shows the co-authorship network connecting the top 25 collaborators of Javier Pérez-Rodríguez. A scholar is included among the top collaborators of Javier Pérez-Rodríguez 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 Javier Pérez-Rodríguez. Javier Pérez-Rodríguez is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 3 | |
| 3 | 6 | |
| 4 | 1 | |
| 5 | 10 | |
| 6 | 6 | |
| 7 | 3 | |
| 8 | 9 | |
| 9 | 17 | |
| 10 | 15 | |
| 11 | 6 | |
| 12 | 42 | |
| 13 | 1 | |
| 14 | 33 | |
| 15 | 18 | |
| 16 | 37 | |
| 17 | 38 | |
| 18 | 4 | |
| 19 | 70 | |
| 20 | 173 |
About Javier Pérez-Rodríguez
Javier Pérez-Rodríguez is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Management Science and Operations Research, having authored 24 papers that have together received 526 indexed citations. Recurring topics across this work include Machine Learning and Data Classification (7 papers), Machine Learning in Bioinformatics (6 papers) and Machine Learning and ELM (5 papers). The work is most often cited by research in Artificial Intelligence (258 citations), Radiology, Nuclear Medicine and Imaging (107 citations) and Computer Vision and Pattern Recognition (79 citations). Javier Pérez-Rodríguez has collaborated with scholars based in Spain, United States and United Kingdom. Frequent co-authors include Nicolás García‐Pedrajas, Aida de Haro-García, Derek M. Fine, David A. Bluemke, Benjamin D. Ehst, María D. García‐Pedrajas, Colin Fyfe, Domingo Ortíz-Boyer, Francisco Fernández‐Navarro and Alejandro de la Fuente. Their work appears in journals such as Bioinformatics, Scientific Reports and Radiology.
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.