Alexander Botzki

2.5k total citations · 3 hit papers
30 papers, 1.6k citations indexed

About

Alexander Botzki is a scholar working on Molecular Biology, Organic Chemistry and Cell Biology. According to data from OpenAlex, Alexander Botzki has authored 30 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Molecular Biology, 6 papers in Organic Chemistry and 5 papers in Cell Biology. Recurrent topics in Alexander Botzki's work include Genomics and Phylogenetic Studies (6 papers), Proteoglycans and glycosaminoglycans research (4 papers) and Carbohydrate Chemistry and Synthesis (4 papers). Alexander Botzki is often cited by papers focused on Genomics and Phylogenetic Studies (6 papers), Proteoglycans and glycosaminoglycans research (4 papers) and Carbohydrate Chemistry and Synthesis (4 papers). Alexander Botzki collaborates with scholars based in Belgium, Germany and United States. Alexander Botzki's co-authors include Łukasz Kreft, Frederik Coppens, Klaas Vandepoele, Michiel Van Bel, Yves Van de Peer, Tim Diels, Emmelien Vancaester, Stefan Dove, Armin Buschauer and Günther Bernhardt and has published in prestigious journals such as Nature, Nucleic Acids Research and Journal of Biological Chemistry.

In The Last Decade

Alexander Botzki

29 papers receiving 1.6k citations

Hit Papers

PLAZA 4.0: an integrative resource for functional, evolut... 2017 2026 2020 2023 2017 2022 2024 100 200 300

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Alexander Botzki Belgium 16 1.0k 472 190 136 122 30 1.6k
Bing Xia China 16 1.5k 1.5× 328 0.7× 144 0.8× 95 0.7× 57 0.5× 31 2.2k
Ye Lu China 22 736 0.7× 659 1.4× 179 0.9× 106 0.8× 80 0.7× 85 1.5k
Wei‐Qing Wang China 24 839 0.8× 686 1.5× 199 1.0× 69 0.5× 101 0.8× 54 1.4k
Jean‐Jacques Sanglier Switzerland 19 1.2k 1.2× 196 0.4× 144 0.8× 209 1.5× 127 1.0× 41 1.7k
Jinhua Cheng South Korea 23 1.0k 1.0× 273 0.6× 104 0.5× 76 0.6× 297 2.4× 71 1.9k
Hye‐Jin Yoon South Korea 23 1.1k 1.1× 425 0.9× 66 0.3× 61 0.4× 88 0.7× 88 1.7k
Tomoyuki Nishimoto Japan 29 1.1k 1.1× 284 0.6× 95 0.5× 175 1.3× 82 0.7× 123 2.2k
Markus Hartl Austria 23 1.2k 1.2× 507 1.1× 160 0.8× 53 0.4× 163 1.3× 52 1.9k

Countries citing papers authored by Alexander Botzki

Since Specialization
Citations

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

Fields of papers citing papers by Alexander Botzki

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Alexander Botzki

This figure shows the co-authorship network connecting the top 25 collaborators of Alexander Botzki. A scholar is included among the top collaborators of Alexander Botzki 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 Alexander Botzki. Alexander Botzki 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.
Piampongsant, Supinya, Miguel Roncoroni, Beatriz Herrera‐Malaver, et al.. (2024). Predicting and improving complex beer flavor through machine learning. Nature Communications. 15(1). 2368–2368. 62 indexed citations breakdown →
2.
Renaud, Olivier, Nathalie Aulner, Audrey Salles, et al.. (2024). Staying on track – Keeping things running in a high‐end scientific imaging core facility. Journal of Microscopy. 294(3). 276–294. 2 indexed citations
3.
Botzki, Alexander, et al.. (2024). Systematic review and meta-analysis of genome-wide pooled CRISPR screens to identify host factors involved in influenza A virus infection. Journal of Virology. 98(5). e0185723–e0185723. 3 indexed citations
5.
6.
Cortona, Andrea Del, et al.. (2021). TRAPID 2.0: a web application for taxonomic and functional analysis of de novo transcriptomes. Nucleic Acids Research. 49(17). e101–e101. 27 indexed citations
7.
Verschaffelt, Pieter, et al.. (2021). Unipept Visualizations: an interactive visualization library for biological data. Bioinformatics. 38(2). 562–563. 3 indexed citations
8.
Roca, Carlos P., Oliver T. Burton, Václav Gergelits, et al.. (2021). AutoSpill is a principled framework that simplifies the analysis of multichromatic flow cytometry data. Nature Communications. 12(1). 2890–2890. 23 indexed citations
9.
Reuver, Richard de, Evelien Dierick, Bartosz Wiernicki, et al.. (2021). ADAR1 interaction with Z-RNA promotes editing of endogenous double-stranded RNA and prevents MDA5-dependent immune activation. Cell Reports. 36(6). 109500–109500. 78 indexed citations
10.
Turinsky, Andrei L., Sam Dupont, Alexander Botzki, et al.. (2020). Navigating the Global Protein–Protein Interaction Landscape Using iRefWeb. Methods in molecular biology. 191–207. 2 indexed citations
11.
Willems, Patrick J., Thomas Van Parys, Sofie Goormachtig, et al.. (2019). The Plant PTM Viewer, a central resource for exploring plant protein modifications. The Plant Journal. 99(4). 752–762. 84 indexed citations
12.
Kreft, Łukasz, Demet Turan, Niels Hulstaert, et al.. (2018). Scop3D: Online Visualization of Mutation Rates on Protein Structure. Journal of Proteome Research. 18(2). 765–769. 1 indexed citations
13.
Kreft, Łukasz, Alexander Botzki, Frederik Coppens, Klaas Vandepoele, & Michiel Van Bel. (2017). PhyD3: a phylogenetic tree viewer with extended phyloXML support for functional genomics data visualization. Bioinformatics. 33(18). 2946–2947. 196 indexed citations
14.
Bel, Michiel Van, Tim Diels, Emmelien Vancaester, et al.. (2017). PLAZA 4.0: an integrative resource for functional, evolutionary and comparative plant genomics. Nucleic Acids Research. 46(D1). D1190–D1196. 324 indexed citations breakdown →
15.
Kreft, Łukasz, Arne Soete, Paco Hulpiau, et al.. (2017). ConTra v3: a tool to identify transcription factor binding sites across species, update 2017. Nucleic Acids Research. 45(W1). W490–W494. 99 indexed citations
16.
Miettinen, Karel, Sabrina Iñigo, Łukasz Kreft, et al.. (2017). The TriForC database: a comprehensive up-to-date resource of plant triterpene biosynthesis. Nucleic Acids Research. 46(D1). D586–D594. 47 indexed citations
17.
Braun, Stephan Alexander, et al.. (2011). Design of benzimidazole- and benzoxazole-2-thione derivatives as inhibitors of bacterial hyaluronan lyase. European Journal of Medicinal Chemistry. 46(9). 4419–4429. 39 indexed citations
18.
Rigden, Daniel J., Alexander Botzki, Ejvis Lamani, et al.. (2006). Design of new benzoxazole-2-thione-derived inhibitors of Streptococcus pneumoniae hyaluronan lyase: structure of a complex with a 2-phenylindole. Glycobiology. 16(8). 757–765. 14 indexed citations
19.
Radons, Jürgen, Stefan Dove, Detlef Neumann, et al.. (2003). The Interleukin 1 (IL-1) Receptor Accessory Protein Toll/IL-1 Receptor Domain. Journal of Biological Chemistry. 278(49). 49145–49153. 29 indexed citations
20.
Böhmer, Frank D., Luchezar Karagyozov, Andrea Uecker, et al.. (2003). A Single Amino Acid Exchange Inverts Susceptibility of Related Receptor Tyrosine Kinases for the ATP Site Inhibitor STI-571. Journal of Biological Chemistry. 278(7). 5148–5155. 52 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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