Mateo Sokač
Impact in
- Health Informatics top 10%
- Artificial Intelligence in Healthcare and Education
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- Cancer Genomics and Diagnostics
Papers in
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- Epigenetics and DNA Methylation 2
- Bioinformatics and Genomic Networks 2
- vaccines and immunoinformatics approaches 2
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- Immune Cell Function and Interaction 2
- interferon and immune responses 2
- Immunotherapy and Immune Responses 2
- Co-authors
- Nicolai J. Birkbak (9 shared papers)Hugo J.W.L. Aerts (3 shared papers)Tafadzwa L. Chaunzwa (1 shared paper)Raymond H. Mak (1 shared paper)Dennis Bontempi (1 shared paper)Suraj Pai (1 shared paper)Simon Bernatz (1 shared paper)Ahmed Hosny (1 shared paper)
- Journals
- Cancers (2 papers)eLife (2 papers)Nature Machine Intelligence (1 paper)International Journal of Molecular Sciences (1 paper)npj Vaccines (1 paper)
- Partner nations
- DenmarkCroatiaUnited States
In The Last Decade
Mateo Sokač
14 papers receiving 146 citations
Mateo Sokač's Hit Papers
Peers
Comparison fields: 5 of 60
- Health Informatics 15
- Cancer Research 28
- Radiology, Nuclear Medicine and Imaging 40
- Oncology 43
- Immunology 24
Countries citing papers authored by Mateo Sokač
This map shows the geographic impact of Mateo Sokač'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 Mateo Sokač with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mateo Sokač more than expected).
Fields of papers citing papers by Mateo Sokač
This network shows the impact of papers produced by Mateo Sokač. 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 Mateo Sokač. The network helps show where Mateo Sokač may publish in the future.
Co-authors
The 25 scholars most cited alongside Mateo Sokač, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Foundation model for cancer imaging biomarkers Hit paper breakdown → | 2024 | 63 |
| 2 | 2022 | 18 | |
| 3 | 2022 | 17 | |
| 4 | 2022 | 12 | |
| 5 | 2016 | 12 | |
| 6 | 2023 | 9 | |
| 7 | 2023 | 5 | |
| 8 | 2024 | 5 | |
| 9 | 2025 | 2 | |
| 10 | 2023 | 2 | |
| 11 | 2022 | 2 | |
| 12 | 2024 | 1 | |
| 13 | 2024 | 1 | |
| 14 | 2021 | 1 | |
| 15 | 2024 | 0 | |
| 16 | 2024 | 0 | |
| 17 | 2025 | 0 | |
| 18 | 2025 | 0 |
About Mateo Sokač
Mateo Sokač is a scholar working on Molecular Biology, Immunology, Cancer Research, Artificial Intelligence and Infectious Diseases, having authored 18 papers that have together received 150 indexed citations. Recurring topics across this work include Cancer Genomics and Diagnostics (4 papers), Epigenetics and DNA Methylation (2 papers), Immune Cell Function and Interaction (2 papers), Cancer Immunotherapy and Biomarkers (2 papers), interferon and immune responses (2 papers), Immunotherapy and Immune Responses (2 papers), Bioinformatics and Genomic Networks (2 papers) and vaccines and immunoinformatics approaches (2 papers). The work is most often cited by research in Health Informatics (15 citations), Cancer Research (28 citations), Radiology, Nuclear Medicine and Imaging (40 citations), Oncology (43 citations) and Immunology (24 citations). Mateo Sokač has collaborated with scholars based in Denmark, Croatia and United States. Frequent co-authors include Nicolai J. Birkbak, Hugo J.W.L. Aerts, Tafadzwa L. Chaunzwa, Raymond H. Mak, Dennis Bontempi, Suraj Pai, Simon Bernatz, Ahmed Hosny, Johanne Ahrenfeldt and Martin R. Jakobsen. Their work appears in journals such as Cancers, eLife, Nature Machine Intelligence, International Journal of Molecular Sciences and npj Vaccines.
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.