O. Tsave

36 papers receiving 1.3k citations

O. Tsave's Hit Papers

Machine Learning and Data Mining Methods in Diabetes Research 2017 · 992 citations
9920+3+6Years since publication250500750

Peers

O. Tsave
Comparison fields: 5 of 149
  • Health Information Management 594
  • Health Informatics 25
  • Artificial Intelligence 470
  • Complementary and alternative medicine 79
  • Inorganic Chemistry 109
Replace Filippo Amato with:
Filippo Amato Italy
Ying Ju China
Chao Che China
Mohammed Abdul Salam Gollapalli Saudi Arabia
Ahmed Sultan Egypt
Anthony N Nguyen Australia
Ni Wang China
Hang Dong China
György Simon United States
Qian Hua Zhu China
O. Tsave relative to Filippo Amato Italy Filippo Amato's profile →
Citations per field
00.5×2×3×4.4×
Filippo Amato · 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 38 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 →
2017992
2 201569
3 201750
4 201649
5 201629
6 201525
7 201519
8 201819
9 202217
10 202015
11 201911
12 201711
13 20179
14 20189
15 20208
16 20197
17 20207
18 20177
19 20246
20 20176

About O. Tsave

O. Tsave is a scholar working on Inorganic Chemistry, Nutrition and Dietetics, Oncology, Health, Toxicology and Mutagenesis and Molecular Biology, having authored 38 papers that have together received 1.4k 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 (6 papers), Metal-Organic Frameworks: Synthesis and Applications (4 papers), Radiomics and Machine Learning in Medical Imaging (4 papers), Chromium effects and bioremediation (4 papers) and AI in cancer detection (4 papers). The work is most often cited by research in Health Information Management (594 citations), Health Informatics (25 citations), Artificial Intelligence (470 citations), Complementary and alternative medicine (79 citations) and Inorganic Chemistry (109 citations). O. Tsave has collaborated with scholars based in Greece, Italy and United Kingdom. Frequent co-authors include Athanasios Salifoglou, Ioannis Kavakiotis, Ioannis P. Vlahavas, Ioanna Chouvarda, Nicos Maglaveras, Maria P. Yavropoulou, John G. Yovos, Catherine Gabriel, Efrosini Kioseoglou and Doxakis Anestakis. Their work appears in journals such as Journal of Inorganic Biochemistry, International Journal of Molecular Sciences, Nano Communication Networks, JBMR Plus and Frontiers in Pharmacology.

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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