Greet De Baets

2.1k total citations
18 papers, 1.0k citations indexed

About

Greet De Baets is a scholar working on Molecular Biology, Physiology and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Greet De Baets has authored 18 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Molecular Biology, 4 papers in Physiology and 3 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Greet De Baets's work include Protein Structure and Dynamics (9 papers), Bioinformatics and Genomic Networks (4 papers) and Advanced Proteomics Techniques and Applications (3 papers). Greet De Baets is often cited by papers focused on Protein Structure and Dynamics (9 papers), Bioinformatics and Genomic Networks (4 papers) and Advanced Proteomics Techniques and Applications (3 papers). Greet De Baets collaborates with scholars based in Belgium, United Kingdom and Spain. Greet De Baets's co-authors include Joost Schymkowitz, Frédéric Rousseau, Joost Van Durme, Joke Reumers, Joaquı́n Dopazo, Sebastian Maurer‐Stroh, Peter Vanhee, Françoise Rousseau-Hans, Rodrigo Gallardo and Charles N. J. Ravarani and has published in prestigious journals such as Nucleic Acids Research, Journal of Biological Chemistry and Nature Communications.

In The Last Decade

Greet De Baets

18 papers receiving 1.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Greet De Baets Belgium 16 772 232 179 128 113 18 1.0k
Mathieu Lavallée‐Adam Canada 21 1.1k 1.4× 168 0.7× 86 0.5× 115 0.9× 92 0.8× 47 1.6k
Jacky Chi Ki Ngo Hong Kong 19 893 1.2× 63 0.3× 47 0.3× 59 0.5× 83 0.7× 45 1.2k
Emiel Michiels Belgium 13 518 0.7× 117 0.5× 179 1.0× 36 0.3× 113 1.0× 19 745
Ira L. Goldknopf United States 20 1.2k 1.6× 149 0.6× 132 0.7× 95 0.7× 41 0.4× 33 1.5k
Uwe Bertsch Germany 22 1.0k 1.3× 144 0.6× 67 0.4× 61 0.5× 13 0.1× 36 1.2k
Masataka Horiuchi Japan 19 689 0.9× 192 0.8× 127 0.7× 44 0.3× 13 0.1× 32 1.4k
Nathan J. Moerke United States 10 948 1.2× 38 0.2× 33 0.2× 68 0.5× 31 0.3× 15 1.2k
Gopal Gunanathan Jayaraj India 16 1.0k 1.3× 166 0.7× 58 0.3× 64 0.5× 18 0.2× 17 1.3k
Jared R. Auclair United States 12 312 0.4× 217 0.9× 408 2.3× 15 0.1× 33 0.3× 34 794
Lars Israel Germany 21 990 1.3× 132 0.6× 66 0.4× 99 0.8× 59 0.5× 28 1.5k

Countries citing papers authored by Greet De Baets

Since Specialization
Citations

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

Fields of papers citing papers by Greet De Baets

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Greet De Baets. 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 Greet De Baets. The network helps show where Greet De Baets may publish in the future.

Co-authorship network of co-authors of Greet De Baets

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

All Works

18 of 18 papers shown
1.
Gogleva, Anna, et al.. (2021). Drug Discovery as a Recommendation Problem: Challenges and Complexities in Biological Decisions. 548–550. 2 indexed citations
2.
Ravarani, Charles N. J., et al.. (2018). High‐throughput discovery of functional disordered regions: investigation of transactivation domains. Molecular Systems Biology. 14(5). e8190–e8190. 81 indexed citations
3.
Váradi, Mihály, Greet De Baets, Wim Vranken, Péter Tompa, & Rita Pancsa. (2017). AmyPro: a database of proteins with validated amyloidogenic regions. Nucleic Acids Research. 46(D1). D387–D392. 66 indexed citations
4.
Ganesan, Ashok, Aleksandra Siekierska, Marijke Brams, et al.. (2016). Structural hot spots for the solubility of globular proteins. Nature Communications. 7(1). 10816–10816. 57 indexed citations
5.
Boeynaems, Steven, Elke Bogaert, Emiel Michiels, et al.. (2016). Drosophila screen connects nuclear transport genes to DPR pathology in c9ALS/FTD. Scientific Reports. 6(1). 20877–20877. 194 indexed citations
6.
Durme, Joost Van, Greet De Baets, Rob van der Kant, et al.. (2016). Solubis: a webserver to reduce protein aggregation through mutation. Protein Engineering Design and Selection. 29(8). 285–289. 57 indexed citations
7.
Baets, Greet De, et al.. (2015). Increased Aggregation Is More Frequently Associated to Human Disease-Associated Mutations Than to Neutral Polymorphisms. PLoS Computational Biology. 11(9). e1004374–e1004374. 37 indexed citations
8.
Baets, Greet De, Joost Van Durme, Rob van der Kant, Joost Schymkowitz, & Frédéric Rousseau. (2015). Solubis: optimize your protein. Bioinformatics. 31(15). 2580–2582. 21 indexed citations
9.
Vaira, Dolorès, Greet De Baets, Yoeri Schrooten, et al.. (2014). Horizontal gene transfer from human host to HIV-1 reverse transcriptase confers drug resistance and partly compensates for replication deficits. Virology. 456-457. 310–318. 5 indexed citations
10.
Couceiro, José R., Rodrigo Gallardo, Frederik De Smet, et al.. (2014). Sequence-dependent Internalization of Aggregating Peptides. Journal of Biological Chemistry. 290(1). 242–258. 19 indexed citations
11.
Baets, Greet De, Joost Van Durme, Frédéric Rousseau, & Joost Schymkowitz. (2014). A Genome-Wide Sequence–Structure Analysis Suggests Aggregation Gatekeepers Constitute an Evolutionary Constrained Functional Class. Journal of Molecular Biology. 426(12). 2405–2412. 33 indexed citations
12.
Ganesan, Ashok, Maja Debulpaep, Hannah Wilkinson, et al.. (2014). Selectivity of Aggregation-Determining Interactions. Journal of Molecular Biology. 427(2). 236–247. 24 indexed citations
13.
Baets, Greet De, Joost Schymkowitz, & Frédéric Rousseau. (2014). Predicting aggregation-prone sequences in proteins. Essays in Biochemistry. 56. 41–52. 45 indexed citations
14.
Vandersteen, Annelies, Marcelo F. Masman, Greet De Baets, et al.. (2012). Molecular Plasticity Regulates Oligomerization and Cytotoxicity of the Multipeptide-length Amyloid-β Peptide Pool. Journal of Biological Chemistry. 287(44). 36732–36743. 38 indexed citations
15.
Vandersteen, Annelies, Ellen Hubin, Rabia Sarroukh, et al.. (2012). A comparative analysis of the aggregation behavior of amyloid‐β peptide variants. FEBS Letters. 586(23). 4088–4093. 61 indexed citations
16.
Siekierska, Aleksandra, Greet De Baets, Joke Reumers, et al.. (2012). α-Galactosidase Aggregation Is a Determinant of Pharmacological Chaperone Efficacy on Fabry Disease Mutants. Journal of Biological Chemistry. 287(34). 28386–28397. 29 indexed citations
17.
Baets, Greet De, Joke Reumers, Javier Delgado, et al.. (2011). An Evolutionary Trade-Off between Protein Turnover Rate and Protein Aggregation Favors a Higher Aggregation Propensity in Fast Degrading Proteins. PLoS Computational Biology. 7(6). e1002090–e1002090. 43 indexed citations
18.
Baets, Greet De, Joost Van Durme, Joke Reumers, et al.. (2011). SNPeffect 4.0: on-line prediction of molecular and structural effects of protein-coding variants. Nucleic Acids Research. 40(D1). D935–D939. 214 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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