Borja Calvo
Impact in
- Cancer Research top 10%
- MicroRNA in disease regulation
- Cancer-related molecular mechanisms research
- Artificial Intelligence top 5%
- Metaheuristic Optimization Algorithms Research
- Machine Learning and Data Classification
Papers in
-
- Machine Learning and Data Classification 8
- Bayesian Modeling and Causal Inference 6
- Metaheuristic Optimization Algorithms Research 5
- Anomaly Detection Techniques and Applications 4
- Bayesian Methods and Mixture Models 3
- Co-authors
- José A. LozanoGuzmán SantaféPedro LarrañagaIñaki InzaRubén ArmañanzasAritz PérezVı́ctor RoblesConcha Bielza
In The Last Decade
Borja Calvo
38 papers receiving 1.3k citations
Hit Papers
Peers
Comparison fields: 5 of 157
- Cancer Research 217
- Artificial Intelligence 380
- Molecular Biology 637
- Computational Theory and Mathematics 123
- Industrial and Manufacturing Engineering 51
Countries citing papers authored by Borja Calvo
This map shows the geographic impact of Borja Calvo'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 Borja Calvo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Borja Calvo more than expected).
Fields of papers citing papers by Borja Calvo
This network shows the impact of papers produced by Borja Calvo. 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 Borja Calvo. The network helps show where Borja Calvo may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Borja Calvo, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2022 | 7 | |
| 3 | 2021 | 4 | |
| 4 | 2020 | 4 | |
| 5 | 2019 | 9 | |
| 6 | 2018 | 1 | |
| 7 | 2016 | 13 | |
| 8 | 2016 | 32 | |
| 9 | 2015 | 6 | |
| 10 | 2015 | 3 | |
| 11 | 2013 | 27 | |
| 12 | 2010 | 2 | |
| 13 | 2009 | 16 | |
| 14 | 2009 | 72 | |
| 15 | 2009 | 204 | |
| 16 | 2008 | 26 | |
| 17 | 2008 | 16 | |
| 18 | 2007 | 15 | |
| 19 | 2002 | 10 | |
| 20 | 1997 | 30 |
About Borja Calvo
Borja Calvo is a scholar working on Artificial Intelligence, Signal Processing, Computational Theory and Mathematics, Computer Vision and Pattern Recognition and Statistics and Probability, having authored 40 papers that have together received 1.4k indexed citations. Recurring topics across this work include Gene expression and cancer classification (8 papers), Machine Learning and Data Classification (8 papers), Bayesian Modeling and Causal Inference (6 papers), Metaheuristic Optimization Algorithms Research (5 papers), Machine Learning in Bioinformatics (4 papers), Face and Expression Recognition (4 papers), Anomaly Detection Techniques and Applications (4 papers) and Bayesian Methods and Mixture Models (3 papers). The work is most often cited by research in Cancer Research (217 citations), Artificial Intelligence (380 citations), Molecular Biology (637 citations), Computational Theory and Mathematics (123 citations) and Industrial and Manufacturing Engineering (51 citations). Borja Calvo has collaborated with scholars based in Spain, France and Australia. Frequent co-authors include José A. Lozano, Guzmán Santafé, Pedro Larrañaga, Iñaki Inza, Rubén Armañanzas, Aritz Pérez, Vı́ctor Robles, Concha Bielza, Roberto Santana and Ekhiñe Irurozki. Their work appears in journals such as Pattern Recognition Letters, Knowledge and Information Systems, Computer Methods and Programs in Biomedicine, Journal of Microencapsulation and Bernoulli.
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.