Maya Topf

9.8k citations
118 papers · 5.7k indexed · 2 hit papers · h-index 45

Maya Topf

116 papers receiving 5.7k citations

Hit Papers

Critical assessment of methods of protein structure predi...2912019202620212023100200300

Peers

Maya Topf
Comparison fields: 5 of 142
  • Structural Biology 880
  • Molecular Biology 4.3k
  • Cell Biology 637
  • Surfaces, Coatings and Films 261
  • Materials Chemistry 1.3k
Replace Takanori Nakane with:
Takanori Nakane Japan
Gunnar F. Schröder Germany
Jasenko Zivanov United Kingdom
Florence Tama Japan
Kliment A. Verba United States
Dari Kimanius United Kingdom
Yuri L. Lyubchenko United States
Xiao‐chen Bai United States
Michael F. Schmid United States
Alexis Rohou United States
Maya Topf relative to Takanori Nakane Japan Takanori Nakane's profile →
Citations per field
00.5×1.5×2.5×
Takanori Nakane · 1×
Citations per year

Countries citing papers authored by Maya Topf

Since Specialization
Citations

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

Fields of papers citing papers by Maya Topf

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Maya Topf, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Maya Topf Line = papers co-authored together Maya Topf links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20250
2 20249
3 20241
4 202414
5 202337
6 202360
7 20238
8 202372
9 20233
10 202223
11
Critical assessment of methods of protein structure prediction (CASP)—Round XIVbreakdown →
2021291
12 20201
13 202050
14
Critical assessment of methods of protein structure prediction (CASP)—Round XIIIbreakdown →
2019330
15 201921
16 201916
17 201775
18 201714
19 201682
20 201020

About Maya Topf

Maya Topf is a scholar working on Structural Biology, Surfaces, Coatings and Films, Molecular Biology, Cell Biology and Materials Chemistry, having authored 118 papers that have together received 5.7k indexed citations. Recurring topics across this work include Enzyme Structure and Function (31 papers), Advanced Electron Microscopy Techniques and Applications (28 papers), RNA and protein synthesis mechanisms (24 papers), Protein Structure and Dynamics (18 papers), RNA modifications and cancer (12 papers), Microtubule and mitosis dynamics (11 papers), Electron and X-Ray Spectroscopy Techniques (11 papers) and Ion channel regulation and function (11 papers). The work is most often cited by research in Structural Biology (880 citations), Molecular Biology (4.3k citations), Cell Biology (637 citations), Surfaces, Coatings and Films (261 citations) and Materials Chemistry (1.3k citations). Maya Topf has collaborated with scholars based in United Kingdom, United States and Germany. Frequent co-authors include Andrej Săli, Andriy Kryshtafovych, Krzysztof Fidelis, John Moult, Torsten Schwede, Wah Chiu, Frank Alber, Daven Vasishtan, Arun Prasad Pandurangan and Agnel Praveen Joseph. Their work appears in journals such as Structure, Proteins Structure Function and Bioinformatics, Nature Communications, Journal of Structural Biology and Bioinformatics.

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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