Anton S. Tkachenko
Impact in
- Aquatic Science top 10%
- Seaweed-derived Bioactive Compounds
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- Erythrocyte Function and Pathophysiology
Papers in
- Physiology 25
- Erythrocyte Function and Pathophysiology 21
-
- Seaweed-derived Bioactive Compounds 12
- Co-authors
- Anatolii I. Onishchenko (50 shared papers)Ondřej Havránek (12 shared papers)Svetlana L. Yefimova (19 shared papers)Dmytro Butov (13 shared papers)Tetyana Chumachenko (7 shared papers)Nataliya S. Kavok (11 shared papers)Pavel O. Maksimchuk (11 shared papers)Ievgen Meniailov (3 shared papers)
In The Last Decade
Anton S. Tkachenko
81 papers receiving 628 citations
Peers
Comparison fields: 5 of 114
- Aquatic Science 54
- Physiology 159
- Modeling and Simulation 23
- Physiology 17
- Health Information Management 13
Countries citing papers authored by Anton S. Tkachenko
This map shows the geographic impact of Anton S. 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 S. 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 S. Tkachenko more than expected).
Fields of papers citing papers by Anton S. Tkachenko
This network shows the impact of papers produced by Anton S. 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 S. Tkachenko. The network helps show where Anton S. Tkachenko may publish in the future.
Co-authors
The 25 scholars most cited alongside Anton S. 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 99 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2023 | 52 | |
| 2 | 2023 | 39 | |
| 3 | The Concept of Developing a Decision Support System for the Epidemic Morbidity Control. | 2020 | 37 |
| 4 | 2022 | 31 | |
| 5 | 2022 | 26 | |
| 6 | 2022 | 24 | |
| 7 | 2024 | 21 | |
| 8 | 2020 | 20 | |
| 9 | 2015 | 20 | |
| 10 | 2021 | 20 | |
| 11 | 2021 | 18 | |
| 12 | 2021 | 17 | |
| 13 | 2021 | 13 | |
| 14 | 2024 | 13 | |
| 15 | Classification and Prediction of Diabetes Disease using Decision Tree Method. | 2021 | 13 |
| 16 | 2018 | 13 | |
| 17 | 2023 | 13 | |
| 18 | 2023 | 12 | |
| 19 | 2022 | 11 | |
| 20 | 2022 | 11 |
About Anton S. Tkachenko
Anton S. Tkachenko is a scholar working on Physiology, Aquatic Science, Otorhinolaryngology, Pulmonary and Respiratory Medicine and Molecular Biology, having authored 99 papers that have together received 645 indexed citations. Recurring topics across this work include Erythrocyte Function and Pathophysiology (21 papers), Seaweed-derived Bioactive Compounds (12 papers), Sinusitis and nasal conditions (9 papers), Advanced Nanomaterials in Catalysis (8 papers), Blood properties and coagulation (8 papers), Cell death mechanisms and regulation (6 papers), Food Industry and Aquatic Biology (5 papers) and Tuberculosis Research and Epidemiology (4 papers). The work is most often cited by research in Aquatic Science (54 citations), Physiology (159 citations), Modeling and Simulation (23 citations), Physiology (17 citations) and Health Information Management (13 citations). Anton S. Tkachenko has collaborated with scholars based in Ukraine, Czechia and Türkiye. Frequent co-authors include Anatolii I. Onishchenko, Ondřej Havránek, Svetlana L. Yefimova, Dmytro Butov, Tetyana Chumachenko, Nataliya S. Kavok, Pavel O. Maksimchuk, Ievgen Meniailov, Vladimir Klochkov and Yevgen O. Posokhov. Their work appears in journals such as Nanotechnology, International Journal of Molecular Sciences, APOPTOSIS, Cell Death and Disease and Biological Trace Element Research.
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.