Jan Byška

742 citations
36 papers · 524 · h-index 11

Impact in

Papers in

Jan Byška

35 papers receiving 521 citations

Peers

Jan Byška
Comparison fields: 5 of 106
  • Biophysics 36
  • Computer Vision and Pattern Recognition 127
  • Molecular Biology 347
  • Human-Computer Interaction 20
  • Biotechnology 26
Replace Charly Empereur‐mot with:
Charly Empereur‐mot Switzerland
Katarína Furmanová Czechia
Jiří Filipovič Czechia
Adam Jurčík Czechia
Wolfgang Heiden Germany
Christoph Grebner Germany
Laura J. Kingsley United States
Kei-Hoi Cheung United States
David S. Goodsell United States
Gabriel David Portugal
Jan Byška relative to Charly Empereur‐mot Switzerland Charly Empereur‐mot's profile →
Citations per field
00.5×6.5×
Charly Empereur‐mot · 1×
Citations per year

Countries citing papers authored by Jan Byška

Since Specialization
Citations

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

Fields of papers citing papers by Jan Byška

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Jan Byška, 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 Jan Byška Line = papers co-authored together Jan Byška links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 36 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2018273
2 201524
3 201523
4 202321
5 202119
6 202218
7 201715
8 201813
9 201711
10 201911
11 202111
12 20209
13 20178
14 20197
15 20206
16 20225
17 20195
18 20195
19 20194
20 20154

About Jan Byška

Jan Byška is a scholar working on Molecular Biology, Computer Vision and Pattern Recognition, Biophysics, Artificial Intelligence and Spectroscopy, having authored 36 papers that have together received 524 indexed citations. Recurring topics across this work include Protein Structure and Dynamics (15 papers), Data Visualization and Analytics (14 papers), Cell Image Analysis Techniques (6 papers), Bioinformatics and Genomic Networks (5 papers), Microbial Metabolic Engineering and Bioproduction (4 papers), Mass Spectrometry Techniques and Applications (4 papers), Genetics, Bioinformatics, and Biomedical Research (2 papers) and Business Process Modeling and Analysis (2 papers). The work is most often cited by research in Biophysics (36 citations), Computer Vision and Pattern Recognition (127 citations), Molecular Biology (347 citations), Human-Computer Interaction (20 citations) and Biotechnology (26 citations). Jan Byška has collaborated with scholars based in Czechia, Norway and Austria. Frequent co-authors include Barbora Kozlíková, Adam Jurčík, Katarína Furmanová, Jiřı́ Damborský, Sérgio M. Marques, David Bednář, Jan Štourač, Ondřej Strnad, Lukáš Daniel and Piia Kokkonen. Their work appears in journals such as IEEE Transactions on Visualization and Computer Graphics, Computer Graphics Forum, BMC Bioinformatics, Computers & Graphics and Computational and Structural Biotechnology Journal.

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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