Torgyn Shaikhina
- Artificial Intelligence top 10%
- Radiology, Nuclear Medicine and Imaging
- Health Information Management top 5%
- Biomedical Engineering
- Transplantation top 5%
- Co-authors
- N. A. KhovanovaNatasha KhovanovaSunil DagaRobert HigginsDavid BriggsKajal K. MallickJames H. JonesDavid Lowe
- Topics
- Renal Transplantation Outcomes and Treatments (3 papers)Medical Imaging and Analysis (2 papers)Artificial Intelligence in Healthcare (2 papers)
- Journals
- Biomedical Signal Processing and ControlArtificial Intelligence in MedicineTransplant International
- Partner nations
- United Kingdom
In The Last Decade
Torgyn Shaikhina
7 papers receiving 606 citations
Hit Papers
Peers
Comparison fields: 5 of 154
- Artificial Intelligence 160
- Radiology, Nuclear Medicine and Imaging 73
- Health Information Management 62
- Biomedical Engineering 61
- Transplantation 59
Countries citing papers authored by Torgyn Shaikhina
This map shows the geographic impact of Torgyn Shaikhina'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 Torgyn Shaikhina with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Torgyn Shaikhina more than expected).
Fields of papers citing papers by Torgyn Shaikhina
This network shows the impact of papers produced by Torgyn Shaikhina. 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 Torgyn Shaikhina. The network helps show where Torgyn Shaikhina may publish in the future.
Co-authorship network of co-authors of Torgyn Shaikhina
This figure shows the co-authorship network connecting the top 25 collaborators of Torgyn Shaikhina. A scholar is included among the top collaborators of Torgyn Shaikhina based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Torgyn Shaikhina. Torgyn Shaikhina is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 202 | |
| 2 | Decision tree and random forest models for outcome prediction in antibody incompatible kidney transplantationbreakdown → | 257 |
| 3 | 83 | |
| 4 | Assessment of IgG subclass significance for early graft rejection and long-term survival in HLA-antibody incompatible renal transplantation : multivariate approach | 1 |
| 5 | 46 | |
| 6 | 10 | |
| 7 | 20 |
About Torgyn Shaikhina
Torgyn Shaikhina is a scholar working on Transplantation, Health Information Management and Nephrology, having authored 7 papers that have together received 619 indexed citations. Recurring topics across this work include Renal Transplantation Outcomes and Treatments (3 papers), Medical Imaging and Analysis (2 papers) and Artificial Intelligence in Healthcare (2 papers). The work is most often cited by research in Transplantation (59 citations), Health Informatics (26 citations) and Health Information Management (62 citations). Torgyn Shaikhina has collaborated with scholars based in United Kingdom. Frequent co-authors include N. A. Khovanova, Natasha Khovanova, Sunil Daga, Robert Higgins, David Briggs, Kajal K. Mallick, James H. Jones, David Lowe, Daniel A. Mitchell and Daniel Zehnder. Their work appears in journals such as Biomedical Signal Processing and Control, Artificial Intelligence in Medicine and Transplant International.
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.