Karel Břinda
- Molecular Medicine top 2%
- Antibiotic Resistance in Bacteria 4
- Pollution top 10%
- Endocrinology top 10%
- Clinical Biochemistry top 10%
- Bacterial Identification and Susceptibility Testing 3
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- Genomics and Phylogenetic Studies 6
- Machine Learning in Bioinformatics 2
- Gene expression and cancer classification 2
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- COVID-19 epidemiological studies 2
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- Natural Language Processing Techniques 2
- Algorithms and Data Compression 2
- Co-authors
- William P. HanageYou CheTong ZhangXiaoqing XuGrégory KucherovYang YuMartin F. PolzMaciej Sykulski
- Journals
- Proceedings of the National Academy of Sciences (2 papers)Bioinformatics (2 papers)Nature Methods (1 paper)
- Partner nations
- United StatesFranceUnited Kingdom
In The Last Decade
Karel Břinda
19 papers receiving 468 citations
Hit Papers
Peers
Comparison fields: 5 of 76
- Molecular Medicine 177
- Applied Microbiology and Biotechnology 24
- Pollution 121
- Endocrinology 46
- Clinical Biochemistry 49
Countries citing papers authored by Karel Břinda
This map shows the geographic impact of Karel Břinda'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 Karel Břinda with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Karel Břinda more than expected).
Fields of papers citing papers by Karel Břinda
This network shows the impact of papers produced by Karel Břinda. 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 Karel Břinda. The network helps show where Karel Břinda may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Karel Břinda, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 4 | |
| 2 | 2024 | 1 | |
| 3 | 2022 | 8 | |
| 4 | 2022 | 36 | |
| 5 | Conjugative plasmids interact with insertion sequences to shape the horizontal transfer of antimicrobial resistance genesbreakdown → | 2021 | 219 |
| 6 | 2021 | 18 | |
| 7 | 2021 | 27 | |
| 8 | 2021 | 1 | |
| 9 | 2020 | 8 | |
| 10 | 2020 | 66 | |
| 11 | 2020 | 9 | |
| 12 | 2020 | 1 | |
| 13 | 2020 | 1 | |
| 14 | 2017 | 1 | |
| 15 | 2017 | 2 | |
| 16 | 2015 | 5 | |
| 17 | 2015 | 54 | |
| 18 | 2013 | 1 | |
| 19 | 2011 | 7 |
About Karel Břinda
Karel Břinda is a scholar working on Molecular Medicine, Clinical Biochemistry and Modeling and Simulation, having authored 19 papers that have together received 469 indexed citations. Recurring topics across this work include Genomics and Phylogenetic Studies (6 papers), Antibiotic Resistance in Bacteria (4 papers), Bacterial Identification and Susceptibility Testing (3 papers), Machine Learning in Bioinformatics (2 papers), COVID-19 epidemiological studies (2 papers), Natural Language Processing Techniques (2 papers), Algorithms and Data Compression (2 papers) and Gene expression and cancer classification (2 papers). The work is most often cited by research in Molecular Medicine (177 citations), Applied Microbiology and Biotechnology (24 citations) and Pollution (121 citations). Karel Břinda has collaborated with scholars based in United States, France and United Kingdom. Frequent co-authors include William P. Hanage, You Che, Tong Zhang, Xiaoqing Xu, Grégory Kucherov, Yang Yu, Martin F. Polz, Maciej Sykulski, Michael Baym and Derek R. MacFadden. Their work appears in journals such as Proceedings of the National Academy of Sciences, Bioinformatics and Nature Methods.
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.