Borja Calvo

2.0k total citations · 1 hit paper
40 papers, 1.4k citations indexed

About

Borja Calvo is a scholar working on Artificial Intelligence, Molecular Biology and Computer Vision and Pattern Recognition. According to data from OpenAlex, Borja Calvo has authored 40 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Artificial Intelligence, 13 papers in Molecular Biology and 5 papers in Computer Vision and Pattern Recognition. Recurrent topics in Borja Calvo's work include Gene expression and cancer classification (8 papers), Machine Learning and Data Classification (8 papers) and Bayesian Modeling and Causal Inference (6 papers). Borja Calvo is often cited by papers focused on Gene expression and cancer classification (8 papers), Machine Learning and Data Classification (8 papers) and Bayesian Modeling and Causal Inference (6 papers). Borja Calvo collaborates with scholars based in Spain, France and Australia. Borja Calvo's 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 and has published in prestigious journals such as Nucleic Acids Research, PLoS ONE and Journal of Food Engineering.

In The Last Decade

Borja Calvo

38 papers receiving 1.3k citations

Hit Papers

Machine learning in bioinformatics 2006 2026 2012 2019 2006 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Borja Calvo Spain 15 637 380 217 123 72 40 1.4k
Saurav Mallik India 27 832 1.3× 447 1.2× 289 1.3× 111 0.9× 79 1.1× 188 2.1k
Henry Han United States 17 332 0.5× 375 1.0× 117 0.5× 165 1.3× 43 0.6× 69 1.2k
Edward Suh United States 18 649 1.0× 299 0.8× 92 0.4× 63 0.5× 148 2.1× 29 1.6k
Minghu Jiang China 16 400 0.6× 323 0.8× 156 0.7× 173 1.4× 20 0.3× 91 1.1k
Rebecka Jörnsten Sweden 19 753 1.2× 127 0.3× 238 1.1× 44 0.4× 92 1.3× 44 1.4k
Wen Zhu China 25 956 1.5× 292 0.8× 303 1.4× 193 1.6× 85 1.2× 97 1.6k
Ken Fukuda Japan 19 827 1.3× 384 1.0× 75 0.3× 48 0.4× 75 1.0× 103 1.6k
Pierre Dupont Belgium 20 489 0.8× 462 1.2× 61 0.3× 153 1.2× 95 1.3× 92 1.5k
Qiao Liu China 23 1.1k 1.7× 212 0.6× 195 0.9× 219 1.8× 99 1.4× 117 1.9k
Cosmin Lazar Belgium 10 1.0k 1.6× 277 0.7× 133 0.6× 71 0.6× 80 1.1× 21 1.6k

Countries citing papers authored by Borja Calvo

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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 of co-authors of Borja Calvo

This figure shows the co-authorship network connecting the top 25 collaborators of Borja Calvo. A scholar is included among the top collaborators of Borja Calvo 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 Borja Calvo. Borja Calvo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Vivanco, María dM, et al.. (2025). Multimodal fusion strategies for survival prediction in breast cancer: A comparative deep learning study. Computational and Structural Biotechnology Journal. 27. 4505–4516.
2.
Ceberio, Josu, et al.. (2022). Bayesian Performance Analysis for Algorithm Ranking Comparison. IEEE Transactions on Evolutionary Computation. 26(6). 1281–1292. 7 indexed citations
3.
Calvo, Borja, et al.. (2021). Ideafix: a decision tree-based method for the refinement of variants in FFPE DNA sequencing data. NAR Genomics and Bioinformatics. 3(4). lqab092–lqab092. 4 indexed citations
4.
Pérez, Aritz, et al.. (2020). Statistical model for reproducibility in ranking-based feature selection. Knowledge and Information Systems. 63(2). 379–410. 4 indexed citations
5.
Flores, José Luis, Borja Calvo, & Aritz Pérez. (2019). Supervised non-parametric discretization based on Kernel density estimation. Pattern Recognition Letters. 128. 496–504. 9 indexed citations
6.
Ceberio, Josu, Borja Calvo, Alexander Mendiburu, & José A. Lozano. (2018). Zorizko instantzia uniformeak sortzen al dira optimizazio konbinatorioan?. EKAIA Euskal Herriko Unibertsitateko Zientzi eta Teknologi Aldizkaria. 261–277. 1 indexed citations
7.
Bediaga, Naiara G., Elena Beristain, Borja Calvo, et al.. (2016). Luminal B breast cancer subtype displays a dicotomic epigenetic pattern. SpringerPlus. 5(1). 623–623. 13 indexed citations
8.
Irurozki, Ekhiñe, Borja Calvo, & José A. Lozano. (2016). PerMallows: AnRPackage for Mallows and Generalized Mallows Models. Journal of Statistical Software. 71(12). 32 indexed citations
9.
Blum, Christian, Borja Calvo, & María J. Blesa. (2015). FrogCOL and FrogMIS: new decentralized algorithms for finding large independent sets in graphs. Swarm Intelligence. 9(2-3). 205–227. 6 indexed citations
10.
Blum, Christian & Borja Calvo. (2015). A matheuristic for the minimum weight rooted arborescence problem. Journal of Heuristics. 21(4). 479–499. 3 indexed citations
11.
Flores, José Luis, Iñaki Inza, Pedro Larrañaga, & Borja Calvo. (2013). A new measure for gene expression biclustering based on non-parametric correlation. Computer Methods and Programs in Biomedicine. 112(3). 367–397. 27 indexed citations
12.
Irurozki, Ekhiñe, Borja Calvo, & José A. Lozano. (2010). A Preprocessing Procedure for Haplotype Inference by Pure Parsimony. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 8(5). 1183–1195. 2 indexed citations
13.
Armañanzas, Rubén, Borja Calvo, Iñaki Inza, et al.. (2009). Microarray Analysis of Autoimmune Diseases by Machine Learning Procedures. IEEE Transactions on Information Technology in Biomedicine. 13(3). 341–350. 16 indexed citations
14.
Inza, Iñaki, Borja Calvo, Rubén Armañanzas, et al.. (2009). Machine Learning: An Indispensable Tool in Bioinformatics. Methods in molecular biology. 593. 25–48. 72 indexed citations
15.
Otaegui, David, Sergio E. Baranzini, Rubén Armañanzas, et al.. (2009). Differential Micro RNA Expression in PBMC from Multiple Sclerosis Patients. PLoS ONE. 4(7). e6309–e6309. 204 indexed citations
16.
Furney, Simon J., Borja Calvo, Pedro Larrañaga, José A. Lozano, & Núria López-Bigas. (2008). Prioritization of candidate cancer genes—an aid to oncogenomic studies. Nucleic Acids Research. 36(18). e115–e115. 26 indexed citations
17.
Calvo, Borja, et al.. (2008). Sunflower oil hydrogenation: Study using response surface methodology. Journal of Food Engineering. 89(4). 370–374. 16 indexed citations
18.
Calvo, Borja, Núria López-Bigas, Simon J. Furney, Pedro Larrañaga, & José A. Lozano. (2007). A partially supervised classification approach to dominant and recessive human disease gene prediction. Computer Methods and Programs in Biomedicine. 85(3). 229–237. 15 indexed citations
19.
Esquisabel, Amaia, et al.. (2002). Preparation and stability of agarose microcapsules containing BCG. Journal of Microencapsulation. 19(2). 237–244. 10 indexed citations
20.
Esquisabel, Amaia, et al.. (1997). Production of BCG alginate-PLL microcapsules by emulsification/internal gelation. Journal of Microencapsulation. 14(5). 627–638. 30 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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