Alberto Tonda

2.1k total citations
70 papers, 951 citations indexed

About

Alberto Tonda is a scholar working on Artificial Intelligence, Molecular Biology and Food Science. According to data from OpenAlex, Alberto Tonda has authored 70 papers receiving a total of 951 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 12 papers in Molecular Biology and 9 papers in Food Science. Recurrent topics in Alberto Tonda's work include Evolutionary Algorithms and Applications (8 papers), Metaheuristic Optimization Algorithms Research (8 papers) and Agriculture Sustainability and Environmental Impact (5 papers). Alberto Tonda is often cited by papers focused on Evolutionary Algorithms and Applications (8 papers), Metaheuristic Optimization Algorithms Research (8 papers) and Agriculture Sustainability and Environmental Impact (5 papers). Alberto Tonda collaborates with scholars based in France, Italy and Netherlands. Alberto Tonda's co-authors include Giovanni Squillero, Alejandro Lopez‐Rincon, Curt A. Bronkhorst, Daniele Versino, Marlet Martínez‐Archundia, Johan Garssen, Aletta D. Kraneveld, Francesco Accatino, Ilija Đjekić and Anet Režek Jambrak and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of Cleaner Production and Scientific Reports.

In The Last Decade

Alberto Tonda

68 papers receiving 927 citations

Peers

Alberto Tonda

ECOLO

Sergio A. Álvarez United States

Jorge Díez Spain

Osval A. Montesinos‐López Mexico

Lili Xu China

Chun‐Yuan Lin Taiwan

Lars St hle Sweden

Yi-Ching Chen Taiwan

Jónathan Heras Spain

Jing Xu China

Gaurav Gupta India

Sergio A. Álvarez United States

Alberto Tonda

152 ×0.7

147 ×0.9

154 ×1.3

138 ×1.6

117 ×1.7

ECOLO

Citations per year, relative to Alberto Tonda Alberto Tonda (= 1×) peers Sergio A. Álvarez

Countries citing papers authored by Alberto Tonda

Since Specialization

Citations

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

Fields of papers citing papers by Alberto Tonda

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Alberto Tonda

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

All Works

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20 of 20 papers shown

Makowski, David, et al.. (2024). Multi-Objective Optimization for Large-scale Allocation of Soybean Crops. Proceedings of the Genetic and Evolutionary Computation Conference. 1174–1182.

Liu, Ting, et al.. (2024). Understanding Parkinson's: The microbiome and machine learning approach. Maturitas. 193. 108185–108185. 2 indexed citations

Kraneveld, Aletta D., et al.. (2024). Methodology for biomarker discovery with reproducibility in microbiome data using machine learning. BMC Bioinformatics. 25(1). 26–26. 6 indexed citations

Garssen, Johan, et al.. (2024). Image Generation with Interactive Evolutionary System using Bayesian Optimization. 2. 1–7. 1 indexed citations

Green, Ashley, et al.. (2023). Optimization models for sustainable insect production chains. Journal of Insects as Food and Feed. 10(5). 865–883. 4 indexed citations

Tonda, Alberto, Patrick Tabeling, Jessica Vanhomwegen, et al.. (2023). An Innovative AI-based primer design tool for precise and accurate detection of SARS-CoV-2 variants of concern. Scientific Reports. 13(1). 15782–15782. 2 indexed citations

Smetana, Sergiy, et al.. (2023). Environmental impact potential of insect production chains for food and feed in Europe. Animal Frontiers. 13(4). 112–120. 19 indexed citations

Barbiero, Pietro, Irma Meijerman, Alberto Tonda, et al.. (2023). A robust mRNA signature obtained via recursive ensemble feature selection predicts the responsiveness of omalizumab in moderate‐to‐severe asthma. Clinical and Translational Allergy. 13(11). e12306–e12306. 5 indexed citations

Shi, Yong, Alberto Tonda, & Francesco Accatino. (2023). Handling ecosystem service trade-offs: the importance of the spatial scale at which no-loss constraints are posed. Landscape Ecology. 38(5). 1163–1175. 8 indexed citations

10.

Perrot, Nathalie, et al.. (2023). Predicting odor profile of food from its chemical composition: Towards an approach based on artificial intelligence and flavorists expertise. Mathematical Biosciences & Engineering. 20(12). 20528–20552. 4 indexed citations

11.

Παπουτσόγλου, Γεώργιος, Sonia Tarazona, Marta B. Lopes, et al.. (2023). Machine learning approaches in microbiome research: challenges and best practices. Frontiers in Microbiology. 14. 1261889–1261889. 49 indexed citations

12.

Sicard, Jason, Sophie Barbe, Rachel Boutrou, et al.. (2023). A primer on predictive techniques for food and bioresources transformation processes. Journal of Food Process Engineering. 46(5). 6 indexed citations

13.

Lopez‐Rincon, Alejandro, Alberto Tonda, Daphne G.J.C. Mulders, et al.. (2021). Classification and specific primer design for accurate detection of SARS-CoV-2 using deep learning. Scientific Reports. 11(1). 947–947. 64 indexed citations

14.

Lopez‐Rincon, Alejandro, Marlet Martínez‐Archundia, Alexander Schönhuth, et al.. (2020). Machine Learning-Based Ensemble Recursive Feature Selection of Circulating miRNAs for Cancer Tumor Classification. Cancers. 12(7). 1785–1785. 40 indexed citations

15.

Gésan-Guiziou, Geneviève, Aude Alaphilippe, Joël Aubin, et al.. (2019). Annotation data about multi criteria assessment methods used in the agri-food research: The french national institute for agricultural research (INRA) experience. SHILAP Revista de lepidopterología. 25. 104204–104204. 2 indexed citations

16.

Tonda, Alberto, Anita J. Grosvenor, Stefan Clerens, & Steven Le Feunteun. (2017). In silico modeling of protein hydrolysis by endoproteases: a case study on pepsin digestion of bovine lactoferrin. Food & Function. 8(12). 4404–4413. 10 indexed citations

17.

Ahanhanzo, Yolaine Glèlè, et al.. (2016). Effets d’une assistance technique ciblée pour l’amélioration de la couverture vaccinale au Cameroun, en Côte d’Ivoire et en Mauritanie en 2014. Bulletin de la Société de pathologie exotique. 109(3). 185–191. 3 indexed citations

18.

Bucur, Doina, et al.. (2015). Optimizing groups of colluding strong attackers in mobile urban communication networks with evolutionary algorithms. Applied Soft Computing. 40. 416–426. 11 indexed citations

19.

Perrot, Nathalie, Hugo de Vries, Évelyne Lutton, et al.. (2015). Some remarks on computational approaches towards sustainable complex agri-food systems. Trends in Food Science & Technology. 48. 88–101. 22 indexed citations

20.

Bucur, Doina, Giovanni Iacca, Giovanni Squillero, & Alberto Tonda. (2014). The impact of topology on energy consumption for collection tree protocols: An experimental assessment through evolutionary computation. Applied Soft Computing. 16. 210–222. 6 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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