Bas Vroling

2.1k total citations
21 papers, 1.0k citations indexed

About

Bas Vroling is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience and Computational Theory and Mathematics. According to data from OpenAlex, Bas Vroling has authored 21 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Molecular Biology, 8 papers in Cellular and Molecular Neuroscience and 5 papers in Computational Theory and Mathematics. Recurrent topics in Bas Vroling's work include Receptor Mechanisms and Signaling (10 papers), Neuropeptides and Animal Physiology (8 papers) and Computational Drug Discovery Methods (5 papers). Bas Vroling is often cited by papers focused on Receptor Mechanisms and Signaling (10 papers), Neuropeptides and Animal Physiology (8 papers) and Computational Drug Discovery Methods (5 papers). Bas Vroling collaborates with scholars based in Netherlands, United Kingdom and Germany. Bas Vroling's co-authors include David E. Gloriam, Vignir Ísberg, Gert Vriend, Christian Munk, Stefan Mordalski, Alexander S. Hauser, Krzysztof Rataj, Andrzej J. Bojarski, Kasper Harpsøe and Coos Baakman and has published in prestigious journals such as Nucleic Acids Research, Nature Communications and Bioinformatics.

In The Last Decade

Bas Vroling

21 papers receiving 995 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Bas Vroling Netherlands 14 842 346 192 132 77 21 1.0k
Kouki Kawakami Japan 19 1.1k 1.3× 516 1.5× 87 0.5× 140 1.1× 53 0.7× 34 1.3k
Saskia Nijmeijer Netherlands 18 626 0.7× 246 0.7× 134 0.7× 145 1.1× 24 0.3× 30 841
Greg J. Reinhart United States 17 878 1.0× 330 1.0× 68 0.4× 93 0.7× 145 1.9× 29 1.3k
Jacob P. Mahoney United States 8 1.4k 1.7× 789 2.3× 95 0.5× 238 1.8× 39 0.5× 11 1.5k
Bryan Clemons United States 7 751 0.9× 233 0.7× 49 0.3× 75 0.6× 74 1.0× 13 999
Liaoyuan A. Hu United States 19 1.2k 1.5× 647 1.9× 47 0.2× 119 0.9× 88 1.1× 27 1.5k
Shawn K. Milano United States 14 897 1.1× 434 1.3× 69 0.4× 52 0.4× 79 1.0× 21 1.1k
Giselle R. Wiggin United Kingdom 9 892 1.1× 496 1.4× 86 0.4× 69 0.5× 54 0.7× 11 1.2k
Hiroki Nakabayashi Japan 16 1.3k 1.6× 435 1.3× 71 0.4× 46 0.3× 88 1.1× 31 1.6k
Oleg G. Kisselev United States 18 1.1k 1.3× 616 1.8× 35 0.2× 69 0.5× 35 0.5× 31 1.3k

Countries citing papers authored by Bas Vroling

Since Specialization
Citations

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

Fields of papers citing papers by Bas Vroling

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Bas Vroling

This figure shows the co-authorship network connecting the top 25 collaborators of Bas Vroling. A scholar is included among the top collaborators of Bas Vroling 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 Bas Vroling. Bas Vroling 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.
Weigmann, Katrin, Bas Vroling, Nathalie Michels, et al.. (2025). Navigating the Sequence-Function Landscape: AI-Driven Discovery of Unseen and Synergistic Mutations in an Amine Transaminase. ACS Catalysis. 15(17). 15121–15131. 1 indexed citations
2.
Molin, Michael Dal, Karin Krumbach, Bas Vroling, et al.. (2023). Beyond rational—biosensor-guided isolation of 100 independently evolved bacterial strain variants and comparative analysis of their genomes. BMC Biology. 21(1). 183–183. 4 indexed citations
3.
Baakman, Coos, et al.. (2023). Understanding structure-guided variant effect predictions using 3D convolutional neural networks. Frontiers in Molecular Biosciences. 10. 1204157–1204157. 8 indexed citations
4.
Płotka, Magdalena, Hildegard Watzlawick, Tom van den Bergh, et al.. (2023). AmiP from hyperthermophilic Thermus parvatiensis prophage is a thermoactive and ultrathermostable peptidoglycan lytic amidase. Protein Science. 32(3). e4585–e4585. 3 indexed citations
5.
Boonen, Rick A.C.M., Wouter W. Wiegant, Bas Vroling, et al.. (2021). Functional Analysis Identifies Damaging CHEK2 Missense Variants Associated with Increased Cancer Risk. Cancer Research. 82(4). 615–631. 33 indexed citations
6.
Boonen, Rick A.C.M., Amélie Rodrigue, Chantal Stoepker, et al.. (2019). Functional analysis of genetic variants in the high-risk breast cancer susceptibility gene PALB2. Nature Communications. 10(1). 5296–5296. 40 indexed citations
7.
Töpf, Ana, Veeramani Preethish‐Kumar, Paulo José Lorenzoni, et al.. (2018). Recessive variants of MuSK are associated with late onset CMS and predominant limb girdle weakness. American Journal of Medical Genetics Part A. 176(7). 1594–1601. 23 indexed citations
8.
Harris, Elizabeth, Marcella Neri, C. Scotton, et al.. (2017). Complex phenotypes associated with STIM1 mutations in both coiled coil and EF-hand domains. Neuromuscular Disorders. 27(9). 861–872. 36 indexed citations
9.
Bergh, Tom van den, Alberto Nobili, Yifeng Tao, et al.. (2017). CorNet: Assigning function to networks of co-evolving residues by automated literature mining. PLoS ONE. 12(5). e0176427–e0176427. 11 indexed citations
10.
Ísberg, Vignir, Stefan Mordalski, Christian Munk, et al.. (2015). GPCRdb: an information system for G protein-coupled receptors. Nucleic Acids Research. 44(D1). D356–D364. 365 indexed citations
11.
Ísberg, Vignir, Bas Vroling, Rob van der Kant, et al.. (2013). GPCRDB: an information system for G protein-coupled receptors. Nucleic Acids Research. 42(D1). D422–D425. 109 indexed citations
12.
Peeters, Miriam C., et al.. (2012). The role of the second and third extracellular loops of the adenosine A1 receptor in activation and allosteric modulation. Biochemical Pharmacology. 84(1). 76–87. 51 indexed citations
13.
Roumen, Luc, et al.. (2011). : The Pitfalls and Challenges of Predicting GPCR-Ligand Interactions.. Pharmaceuticals Policy and Law. 4(9). 1196–1215. 1 indexed citations
14.
Vroling, Bas, D. J. Thorne, Philip McDermott, et al.. (2011). Integrating GPCR-specific information with full text articles. BMC Bioinformatics. 12(1). 362–362. 8 indexed citations
15.
Vroling, Bas, D. J. Thorne, Philip McDermott, et al.. (2011). NucleaRDB: information system for nuclear receptors. Nucleic Acids Research. 40(D1). D377–D380. 18 indexed citations
16.
Sanders, Marijn P. A., Stefan Verhoeven, Chris de Graaf, et al.. (2011). Snooker: A Structure-Based Pharmacophore Generation Tool Applied to Class A GPCRs. Journal of Chemical Information and Modeling. 51(9). 2277–2292. 42 indexed citations
17.
Boxtel, Ruben van, Bas Vroling, Pim W. Toonen, et al.. (2010). Systematic generation of in vivo G protein-coupled receptor mutants in the rat. The Pharmacogenomics Journal. 11(5). 326–336. 17 indexed citations
18.
Vroling, Bas, Mark A Sanders, Coos Baakman, et al.. (2010). GPCRDB: information system for G protein-coupled receptors. Nucleic Acids Research. 39(Database). D309–D319. 113 indexed citations
19.
Khelashvili, George, Kevin C. Dorff, Jufang Shan, et al.. (2010). GPCR-OKB: the G Protein Coupled Receptor Oligomer Knowledge Base. Bioinformatics. 26(14). 1804–1805. 64 indexed citations
20.
Venselaar, Hanka, Robbie P. Joosten, Bas Vroling, et al.. (2009). Homology modelling and spectroscopy, a never-ending love story. European Biophysics Journal. 39(4). 551–563. 47 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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