Federico Divina

1.7k total citations
52 papers, 929 citations indexed

About

Federico Divina is a scholar working on Molecular Biology, Artificial Intelligence and Electrical and Electronic Engineering. According to data from OpenAlex, Federico Divina has authored 52 papers receiving a total of 929 indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Molecular Biology, 20 papers in Artificial Intelligence and 8 papers in Electrical and Electronic Engineering. Recurrent topics in Federico Divina's work include Gene expression and cancer classification (13 papers), Evolutionary Algorithms and Applications (10 papers) and Energy Load and Power Forecasting (7 papers). Federico Divina is often cited by papers focused on Gene expression and cancer classification (13 papers), Evolutionary Algorithms and Applications (10 papers) and Energy Load and Power Forecasting (7 papers). Federico Divina collaborates with scholars based in Spain, Paraguay and Netherlands. Federico Divina's co-authors include Miguel García-Torres, Jesús S. Aguilar–Ruiz, Francisco Gómez-Vela, José Luis Vázquez Noguera, J. F. Torres, Paul Vogt, Raúl Giráldez, Elena Marchiori, Beatriz Pontes and Christian E. Schaerer and has published in prestigious journals such as SHILAP Revista de lepidopterología, Bioinformatics and IEEE Access.

In The Last Decade

Federico Divina

48 papers receiving 878 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Federico Divina Spain 13 295 284 209 143 84 52 929
Francisco Gómez-Vela Spain 10 160 0.5× 224 0.8× 192 0.9× 129 0.9× 78 0.9× 37 756
Kamal Taha United Arab Emirates 18 483 1.6× 164 0.6× 162 0.8× 43 0.3× 29 0.3× 90 1.4k
Zhiping Wang China 17 112 0.4× 153 0.5× 107 0.5× 51 0.4× 23 0.3× 93 928
Prashant Singh Rana India 16 200 0.7× 90 0.3× 147 0.7× 34 0.2× 34 0.4× 103 901
Qiang Gao China 18 220 0.7× 33 0.1× 235 1.1× 194 1.4× 142 1.7× 97 1.1k
David Opeoluwa Oyewola Nigeria 15 202 0.7× 48 0.2× 124 0.6× 74 0.5× 18 0.2× 38 872
Emmanuel Gbenga Dada Nigeria 15 415 1.4× 47 0.2× 149 0.7× 53 0.4× 35 0.4× 52 1.3k
Chia‐Hung Wang Taiwan 15 65 0.2× 268 0.9× 126 0.6× 93 0.7× 39 0.5× 69 875
Mariá C. V. Nascimento Brazil 16 213 0.7× 60 0.2× 63 0.3× 31 0.2× 53 0.6× 55 723
Yang Han China 13 182 0.6× 61 0.2× 118 0.6× 39 0.3× 40 0.5× 44 910

Countries citing papers authored by Federico Divina

Since Specialization
Citations

This map shows the geographic impact of Federico Divina'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 Federico Divina with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Federico Divina more than expected).

Fields of papers citing papers by Federico Divina

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Federico Divina. 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 Federico Divina. The network helps show where Federico Divina may publish in the future.

Co-authorship network of co-authors of Federico Divina

This figure shows the co-authorship network connecting the top 25 collaborators of Federico Divina. A scholar is included among the top collaborators of Federico Divina 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 Federico Divina. Federico Divina 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.
Gómez-Vela, Francisco, et al.. (2025). BinRec: addressing data sparsity and cold-start challenges in recommender systems with biclustering. Applied Intelligence. 55(12).
2.
Divina, Federico, et al.. (2025). Enhancing R-loop prediction with high-throughput sequencing data. NAR Genomics and Bioinformatics. 7(2). lqaf077–lqaf077. 1 indexed citations
3.
Divina, Federico, et al.. (2024). A stacking ensemble learning for Iberian pigs activity prediction: a time series forecasting approach. AIMS Mathematics. 9(5). 13358–13384. 1 indexed citations
4.
García-Torres, Miguel, et al.. (2024). Machine learning for electric energy consumption forecasting: Application to the Paraguayan system. Logic Journal of IGPL. 32(6). 1048–1072. 1 indexed citations
5.
Mello-Román, Julio César, et al.. (2023). Dataset of fundus images for the diagnosis of ocular toxoplasmosis. Data in Brief. 48. 109056–109056. 9 indexed citations
6.
7.
García-Torres, Miguel, Roberto Ruíz, & Federico Divina. (2022). Evolutionary feature selection on high dimensional data using a search space reduction approach. Engineering Applications of Artificial Intelligence. 117. 105556–105556. 24 indexed citations
8.
García-Torres, Miguel, Francisco Gómez-Vela, Federico Divina, et al.. (2022). Analysis of Electric Energy Consumption Profiles Using a Machine Learning Approach: A Paraguayan Case Study. Electronics. 11(2). 267–267. 9 indexed citations
9.
Martínez‐García, Pedro Manuel, Miguel García-Torres, Federico Divina, et al.. (2021). Genome-wide prediction of topoisomerase IIβ binding by architectural factors and chromatin accessibility. PLoS Computational Biology. 17(1). e1007814–e1007814. 7 indexed citations
10.
García-Torres, Miguel, et al.. (2021). Technical analysis strategy optimization using a machine learning approach in stock market indices. Knowledge-Based Systems. 225. 107119–107119. 93 indexed citations
11.
Noguera, José Luis Vázquez, et al.. (2021). A Trust-Based Methodology to Evaluate Deep Learning Models for Automatic Diagnosis of Ocular Toxoplasmosis from Fundus Images. Diagnostics. 11(11). 1951–1951. 8 indexed citations
12.
Gómez-Vela, Francisco, et al.. (2020). Computational Analysis of the Global Effects of Ly6E in the Immune Response to Coronavirus Infection Using Gene Networks. Genes. 11(7). 831–831. 10 indexed citations
13.
Pinto-Roa, Diego P., et al.. (2019). Map-Elites Algorithm for Features Selection Problem.. 1 indexed citations
14.
García-Torres, Miguel, et al.. (2018). Understanding a multivariate semi-metric in the search strategies for attributes subset selection. Proceeding Series of the Brazilian Society of Computational and Applied Mathematics. 6(2). 1 indexed citations
15.
García-Torres, Miguel, et al.. (2015). Feature Selection via Approximated Markov Blankets Using the CFS Method. 38–43. 8 indexed citations
16.
Bacardit, Jaume, Paweł Widera, Alfonso E. Márquez-Chamorro, et al.. (2012). Contact map prediction using a large-scale ensemble of rule sets and the fusion of multiple predicted structural features. Bioinformatics. 28(19). 2441–2448. 29 indexed citations
17.
Divina, Federico, Beatriz Pontes, Raúl Giráldez, & Jesús S. Aguilar–Ruiz. (2011). An effective measure for assessing the quality of biclusters. Computers in Biology and Medicine. 42(2). 245–256. 33 indexed citations
18.
Divina, Federico. (2006). Evolutionary concept learning in first order logic: an overview. AI Communications. 19(1). 13–33. 1 indexed citations
19.
Divina, Federico & Elena Marchiori. (2002). Evolutionary concept learning. Data Archiving and Networked Services (DANS). 343–350. 15 indexed citations
20.
Divina, Federico & Elena Marchiori. (2001). Knowledge based evolutionary programming for inductive learning in first-order logic. Genetic and Evolutionary Computation Conference. 173–173. 2 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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