Alejandro Lopez‐Rincon

1.1k total citations
36 papers, 601 citations indexed

About

Alejandro Lopez‐Rincon is a scholar working on Molecular Biology, Cognitive Neuroscience and Physiology. According to data from OpenAlex, Alejandro Lopez‐Rincon has authored 36 papers receiving a total of 601 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Molecular Biology, 6 papers in Cognitive Neuroscience and 5 papers in Physiology. Recurrent topics in Alejandro Lopez‐Rincon's work include SARS-CoV-2 and COVID-19 Research (4 papers), Gut microbiota and health (4 papers) and MicroRNA in disease regulation (3 papers). Alejandro Lopez‐Rincon is often cited by papers focused on SARS-CoV-2 and COVID-19 Research (4 papers), Gut microbiota and health (4 papers) and MicroRNA in disease regulation (3 papers). Alejandro Lopez‐Rincon collaborates with scholars based in Netherlands, France and Mexico. Alejandro Lopez‐Rincon's co-authors include Alberto Tonda, Peter M. Schantz, Ana Flisser, Elsa Sarti, Marianna Wilson, Jacquelin M. Roberts, Agustín Plancarte, Johan Garssen, Aletta D. Kraneveld and Marlet Martínez‐Archundia and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and Frontiers in Immunology.

In The Last Decade

Alejandro Lopez‐Rincon

32 papers receiving 582 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Alejandro Lopez‐Rincon Netherlands 12 193 151 131 113 56 36 601
Sebastian Meller Germany 20 39 0.2× 528 3.5× 100 0.8× 12 0.1× 38 0.7× 67 1.2k
Daisuke Kimura Japan 16 30 0.2× 98 0.6× 19 0.1× 120 1.1× 58 1.0× 80 824
J Schwartz United States 15 85 0.4× 52 0.3× 408 3.1× 39 0.3× 24 0.4× 32 739
Andrés Villa Argentina 16 276 1.4× 130 0.9× 32 0.2× 133 1.2× 2 0.0× 64 945
Xingang Yu China 9 11 0.1× 77 0.5× 36 0.3× 59 0.5× 37 0.7× 25 444
Jae Hyuk Shin South Korea 23 351 1.8× 402 2.7× 624 4.8× 7 0.1× 6 0.1× 68 1.8k
Carol J. Cox United States 11 38 0.2× 310 2.1× 44 0.3× 10 0.1× 3 0.1× 16 900
Oliver Mueller Germany 19 121 0.6× 179 1.2× 73 0.6× 6 0.1× 2 0.0× 46 1.1k
Julia H. Chariker United States 17 42 0.2× 180 1.2× 83 0.6× 3 0.0× 26 0.5× 56 667

Countries citing papers authored by Alejandro Lopez‐Rincon

Since Specialization
Citations

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

Fields of papers citing papers by Alejandro Lopez‐Rincon

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Alejandro Lopez‐Rincon. 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 Alejandro Lopez‐Rincon. The network helps show where Alejandro Lopez‐Rincon may publish in the future.

Co-authorship network of co-authors of Alejandro Lopez‐Rincon

This figure shows the co-authorship network connecting the top 25 collaborators of Alejandro Lopez‐Rincon. A scholar is included among the top collaborators of Alejandro Lopez‐Rincon 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 Alejandro Lopez‐Rincon. Alejandro Lopez‐Rincon 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.
Garssen, Johan, et al.. (2024). A robust microbiome signature for autism spectrum disorder across different studies using machine learning. Scientific Reports. 14(1). 814–814. 19 indexed citations
2.
Liu, Ting, et al.. (2024). Understanding Parkinson's: The microbiome and machine learning approach. Maturitas. 193. 108185–108185. 2 indexed citations
3.
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
4.
Lopez‐Rincon, Alejandro, et al.. (2024). Exploring Human Perception of AI-Generated Artworks. 1–6. 1 indexed citations
5.
6.
Garssen, Johan, et al.. (2024). Image Generation with Interactive Evolutionary System using Bayesian Optimization. 2. 1–7. 1 indexed citations
7.
Perez‐Pardo, Paula, et al.. (2023). USING MACHINE LEARNING FOR DRUG DISCOVERY IN IBD. SPIRE - Sciences Po Institutional REpository. 1 indexed citations
8.
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
9.
Lopez‐Rincon, Alejandro, et al.. (2023). In Silico Screening of Drugs That Target Different Forms of E Protein for Potential Treatment of COVID-19. Pharmaceuticals. 16(2). 296–296. 3 indexed citations
10.
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
11.
Benner, Marilen, Dorien Feyaerts, Alejandro Lopez‐Rincon, et al.. (2022). A combination of immune cell types identified through ensemble machine learning strategy detects altered profile in recurrent pregnancy loss: a pilot study. PubMed. 3(2). 166–173. 7 indexed citations
12.
13.
Yim, Andrew Y. F. Li, Peter Henneman, Anje A. te Velde, et al.. (2021). Recursive ensemble feature selection provides a robust mRNA expression signature for myalgic encephalomyelitis/chronic fatigue syndrome. Scientific Reports. 11(1). 4541–4541. 18 indexed citations
14.
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
15.
Benner, Marilen, Alejandro Lopez‐Rincon, Johan Garssen, et al.. (2021). Antibiotic Intervention Affects Maternal Immunity During Gestation in Mice. Frontiers in Immunology. 12. 685742–685742. 9 indexed citations
16.
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
17.
Lopez‐Rincon, Alejandro, et al.. (2019). Function Based Brain Modeling and Simulation of an Ischemic Region in Post-Stroke Patients using the Bidomain. Journal of Neuroscience Methods. 331. 108464–108464. 5 indexed citations
18.
Lopez‐Rincon, Alejandro & Shingo Shimoda. (2016). The inverse problem in electroencephalography using the bidomain model of electrical activity. Journal of Neuroscience Methods. 274. 94–105. 15 indexed citations
19.
Lopez‐Rincon, Alejandro, et al.. (2015). On 3D numerical inverse problems for the bidomain model in electrocardiology. Computers & Mathematics with Applications. 69(4). 255–274. 13 indexed citations
20.
Sarti, Elsa, Peter M. Schantz, Agustín Plancarte, et al.. (1992). Prevalence and Risk Factors for Taenia Solium Taeniasis and Cysticercosis in Humans and Pigs in a Village in Morelos, Mexico. American Journal of Tropical Medicine and Hygiene. 46(6). 677–685. 172 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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