Marcos Gestal

687 total citations
40 papers, 402 citations indexed

About

Marcos Gestal is a scholar working on Molecular Biology, Artificial Intelligence and Computational Theory and Mathematics. According to data from OpenAlex, Marcos Gestal has authored 40 papers receiving a total of 402 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Molecular Biology, 13 papers in Artificial Intelligence and 11 papers in Computational Theory and Mathematics. Recurrent topics in Marcos Gestal's work include Evolutionary Algorithms and Applications (9 papers), Computational Drug Discovery Methods (8 papers) and Advanced Chemical Sensor Technologies (7 papers). Marcos Gestal is often cited by papers focused on Evolutionary Algorithms and Applications (9 papers), Computational Drug Discovery Methods (8 papers) and Advanced Chemical Sensor Technologies (7 papers). Marcos Gestal collaborates with scholars based in Spain, United States and United Kingdom. Marcos Gestal's co-authors include Julián Dorado, Alejandro Pazos, Cristian R. Munteanu, Carlos Fernández-Lozano, Juan R. Rabuñal, J.M. Andrade, M.P. Gómez-Carracedo, Humberto González‐Díaz, José A. Seoane and Vanessa Aguiar‐Pulido and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and International Journal of Molecular Sciences.

In The Last Decade

Marcos Gestal

36 papers receiving 393 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Marcos Gestal Spain 13 131 91 59 53 45 40 402
Peiliang Zhang China 11 136 1.0× 77 0.8× 80 1.4× 13 0.2× 51 1.1× 38 418
Anasua Sarkar India 12 181 1.4× 105 1.2× 76 1.3× 7 0.1× 65 1.4× 48 622
Nereida Rodríguez-Fernández Spain 7 128 1.0× 175 1.9× 49 0.8× 9 0.2× 23 0.5× 15 517
Mahdi Vasighi Iran 10 89 0.7× 46 0.5× 93 1.6× 79 1.5× 45 1.0× 22 437
Haixia Long China 13 194 1.5× 67 0.7× 136 2.3× 16 0.3× 27 0.6× 45 536
Limeng Pu United States 12 215 1.6× 211 2.3× 38 0.6× 7 0.1× 54 1.2× 27 419
Zhaoxian Zhou United States 9 95 0.7× 135 1.5× 95 1.6× 10 0.2× 23 0.5× 35 416
А. И. Белоусов Germany 2 48 0.4× 42 0.5× 36 0.6× 170 3.2× 107 2.4× 4 355

Countries citing papers authored by Marcos Gestal

Since Specialization
Citations

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

Fields of papers citing papers by Marcos Gestal

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Marcos Gestal

This figure shows the co-authorship network connecting the top 25 collaborators of Marcos Gestal. A scholar is included among the top collaborators of Marcos Gestal 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 Marcos Gestal. Marcos Gestal 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.
Gestal, Marcos. (2022). Special Issue on Applied Artificial Neural Networks. Applied Sciences. 12(19). 9551–9551. 1 indexed citations
2.
Gestal, Marcos, et al.. (2021). An Analysis of the Current Implementations Based on the WebAuthn and FIDO Authentication Standards. MDPI (MDPI AG). 56–56. 3 indexed citations
3.
Arrasate, Sonia, et al.. (2020). Prediction of Antimalarial Drug-Decorated Nanoparticle Delivery Systems with Random Forest Models. Biology. 9(8). 198–198. 33 indexed citations
4.
Munteanu, Cristian R., Marcos Gestal, Juan R. Rabuñal, et al.. (2020). Net-Net AutoML Selection of Artificial Neural Network Topology for Brain Connectome Prediction. Applied Sciences. 10(4). 1308–1308. 1 indexed citations
5.
Munteanu, Cristian R., et al.. (2019). Improvement of Epitope Prediction Using Peptide Sequence Descriptors and Machine Learning. International Journal of Molecular Sciences. 20(18). 4362–4362. 5 indexed citations
6.
Fernández-Lozano, Carlos, José A. Seoane, Marcos Gestal, et al.. (2016). Texture analysis in gel electrophoresis images using an integrative kernel-based approach. Scientific Reports. 6(1). 19256–19256. 14 indexed citations
7.
Fernández-Lozano, Carlos, et al.. (2014). Improving enzyme regulatory protein classification by means of SVM-RFE feature selection. Molecular BioSystems. 10(5). 1063–1071. 20 indexed citations
8.
Fernández-Lozano, Carlos, Marcos Gestal, Humberto González‐Díaz, et al.. (2014). Markov mean properties for cell death-related protein classification. Journal of Theoretical Biology. 349. 12–21. 11 indexed citations
9.
Aguiar‐Pulido, Vanessa, Marcos Gestal, Maykel Cruz‐Monteagudo, et al.. (2013). Evolutionary Computation and QSAR Research. Current Computer - Aided Drug Design. 9(2). 206–225. 24 indexed citations
10.
Fernández-Lozano, Carlos, et al.. (2013). Kernel-Based Feature Selection Techniques for Transport Proteins Based on Star Graph Topological Indices. Current Topics in Medicinal Chemistry. 13(14). 1681–1691. 14 indexed citations
11.
Aguiar‐Pulido, Vanessa, Marcos Gestal, Carlos Fernández-Lozano, Daniel Rivero, & Cristian R. Munteanu. (2013). Applied Computational Techniques on Schizophrenia Using Genetic Mutations. Current Topics in Medicinal Chemistry. 13(5). 675–684. 3 indexed citations
12.
Fernández-Lozano, Carlos, Marcos Gestal, J.M. Andrade, et al.. (2013). Hybrid Model Based on Genetic Algorithms and SVM Applied to Variable Selection within Fruit Juice Classification. The Scientific World JOURNAL. 2013(1). 982438–982438. 19 indexed citations
13.
14.
Aguiar‐Pulido, Vanessa, José A. Seoane, Marcos Gestal, & Julián Dorado. (2012). Exploring Patterns of Epigenetic Information with Data Mining Techniques. Current Pharmaceutical Design. 19(4). 779–789. 10 indexed citations
15.
Gestal, Marcos, et al.. (2011). Implementation of the predator-prey approach in Genetic Algorithms with grouping into species for solving multimodal problems. Iberian Conference on Information Systems and Technologies. 1–6.
16.
Gestal, Marcos, Daniel Rivero, Juan R. Rabuñal, Julián Dorado, & Alejandro Pazos. (2010). Introducción a los algoritmos genéticos y la programación genética. 5 indexed citations
17.
Gómez-Carracedo, M.P., Marcos Gestal, Julián Dorado, & J.M. Andrade. (2007). Chemically driven variable selection by focused multimodal genetic algorithms in mid-IR spectra. Analytical and Bioanalytical Chemistry. 389(7-8). 2331–2342. 5 indexed citations
18.
Gómez-Carracedo, M.P., Marcos Gestal, Julián Dorado, & J.M. Andrade. (2007). Linking chemical knowledge and genetic algorithms using two populations and focused multimodal search. Chemometrics and Intelligent Laboratory Systems. 87(2). 173–184. 7 indexed citations
19.
Rabuñal, Juan R., Julián Dorado, Miguel A. Varela, Daniel Rivero, & Marcos Gestal. (2004). Distributed genetic programming by an object oriented system using java and corba.. 180 ( Pt 1). 434–439.
20.
Gestal, Marcos, M.P. Gómez-Carracedo, J.M. Andrade, et al.. (2004). Classification of apple beverages using artificial neural networks with previous variable selection. Analytica Chimica Acta. 524(1-2). 225–234. 45 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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