O. Tsave

33 papers receiving 1.2k citations

O. Tsave's Hit Papers

Machine Learning and Data Mining Methods in Diabetes Research 2017 · 849 citations
8490+3+6Years since publication250500750

Peers

O. Tsave
Comparison fields: 5 of 149
  • Health Information Management 558
  • Health Informatics 28
  • Artificial Intelligence 456
  • Complementary and alternative medicine 75
  • Endocrinology, Diabetes and Metabolism 128
Replace Petr Vaňhara with:
Petr Vaňhara Czechia
Filippo Amato Italy
Ying Ju China
Chi‐Chang Chang Taiwan
Ni Wang China
Mohammed Gollapalli Saudi Arabia
Qian Zhu China
Hang Dong China
Arianna Dagliati Italy
Md. Ashraful Alam Bangladesh
O. Tsave relative to Petr Vaňhara Czechia Petr Vaňhara's profile →
Citations per field
00.5×6.7×
Petr Vaňhara · 1×
Citations per year

Countries citing papers authored by O. Tsave

Since Specialization
Citations

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

Fields of papers citing papers by O. Tsave

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 34 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Machine Learning and Data Mining Methods in Diabetes Research
Hit paper breakdown →
2017849
2 201561
3 201751
4 201642
5 201629
6 201524
7 201519
8 201817
9 202014
10 202214
11 201710
12 20178
13 20198
14 20197
15 20177
16 20187
17 20207
18 20176
19 20204
20 20164

About O. Tsave

O. Tsave is a scholar working on Inorganic Chemistry, Molecular Biology, Oncology, Nutrition and Dietetics and Biomedical Engineering, having authored 34 papers that have together received 1.2k indexed citations. Recurring topics across this work include Metal complexes synthesis and properties (9 papers), Vanadium and Halogenation Chemistry (8 papers), Trace Elements in Health (8 papers), Molecular Communication and Nanonetworks (5 papers), Metal-Organic Frameworks: Synthesis and Applications (4 papers), Chromium effects and bioremediation (4 papers), MicroRNA in disease regulation (3 papers) and Wireless Body Area Networks (3 papers). The work is most often cited by research in Health Information Management (558 citations), Health Informatics (28 citations), Artificial Intelligence (456 citations), Complementary and alternative medicine (75 citations) and Endocrinology, Diabetes and Metabolism (128 citations). O. Tsave has collaborated with scholars based in Greece, Romania and United Kingdom. Frequent co-authors include Athanasios Salifoglou, Ioannis Kavakiotis, Ioannis Vlahavas, Nicos Maglaveras, Ioanna Chouvarda, Maria P. Yavropoulou, John G. Yovos, Catherine Gabriel, Doxakis Anestakis and Savvas Petanidis. Their work appears in journals such as Journal of Inorganic Biochemistry, International Journal of Molecular Sciences, Nano Communication Networks, Materials Advances and New Journal of Chemistry.

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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