Anton Tkachenko
Impact in
- Aquatic Science top 10%
- Seaweed-derived Bioactive Compounds
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- Erythrocyte Function and Pathophysiology
Papers in
- Physiology 23
- Erythrocyte Function and Pathophysiology 20
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- Cell death mechanisms and regulation 6
- Co-authors
- Anatolii Onishchenko (50 shared papers)Ondřej Havránek (10 shared papers)Svetlana Yefimova (19 shared papers)Tetyana Chumachenko (7 shared papers)Dmytro Butov (14 shared papers)Pavel Maksimchuk (11 shared papers)Nataliya Kavok (11 shared papers)Ievgen Meniailov (3 shared papers)
In The Last Decade
Anton Tkachenko
76 papers receiving 536 citations
Peers
Comparison fields: 5 of 114
- Aquatic Science 51
- Physiology 148
- Modeling and Simulation 23
- Physiology 16
- Health Information Management 12
Countries citing papers authored by Anton Tkachenko
This map shows the geographic impact of Anton Tkachenko'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 Anton Tkachenko with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Anton Tkachenko more than expected).
Fields of papers citing papers by Anton Tkachenko
This network shows the impact of papers produced by Anton Tkachenko. 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 Anton Tkachenko. The network helps show where Anton Tkachenko may publish in the future.
Co-authors
The 25 scholars most cited alongside Anton Tkachenko, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 94 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2023 | 43 | |
| 2 | The Concept of Developing a Decision Support System for the Epidemic Morbidity Control. | 2020 | 36 |
| 3 | 2023 | 33 | |
| 4 | 2022 | 30 | |
| 5 | 2022 | 25 | |
| 6 | 2022 | 24 | |
| 7 | 2015 | 19 | |
| 8 | 2020 | 19 | |
| 9 | 2021 | 18 | |
| 10 | 2021 | 18 | |
| 11 | 2021 | 17 | |
| 12 | 2024 | 14 | |
| 13 | 2018 | 13 | |
| 14 | 2024 | 13 | |
| 15 | 2021 | 13 | |
| 16 | Classification and Prediction of Diabetes Disease using Decision Tree Method. | 2021 | 12 |
| 17 | 2022 | 11 | |
| 18 | 2022 | 11 | |
| 19 | 2020 | 9 | |
| 20 | 2023 | 9 |
About Anton Tkachenko
Anton Tkachenko is a scholar working on Physiology, Molecular Biology, Aquatic Science, Materials Chemistry and Pulmonary and Respiratory Medicine, having authored 94 papers that have together received 554 indexed citations. Recurring topics across this work include Erythrocyte Function and Pathophysiology (20 papers), Seaweed-derived Bioactive Compounds (12 papers), Sinusitis and nasal conditions (9 papers), Blood properties and coagulation (8 papers), Advanced Nanomaterials in Catalysis (8 papers), Cell death mechanisms and regulation (6 papers), Tuberculosis Research and Epidemiology (6 papers) and Food Industry and Aquatic Biology (5 papers). The work is most often cited by research in Aquatic Science (51 citations), Physiology (148 citations), Modeling and Simulation (23 citations), Physiology (16 citations) and Health Information Management (12 citations). Anton Tkachenko has collaborated with scholars based in Ukraine, Czechia and Türkiye. Frequent co-authors include Anatolii Onishchenko, Ondřej Havránek, Svetlana Yefimova, Tetyana Chumachenko, Dmytro Butov, Pavel Maksimchuk, Nataliya Kavok, Ievgen Meniailov, Vladimir Klochkov and Yevgen O. Posokhov. Their work appears in journals such as Nanotechnology, International Journal of Molecular Sciences, APOPTOSIS, Biological Trace Element Research and Current Microbiology.
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.