David Iberri
Impact in
- Communication top 5%
- Wikis in Education and Collaboration
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- Multiple Myeloma Research and Treatments
- Blood groups and transfusion
Papers in
- Hematology 13
- Multiple Myeloma Research and Treatments 8
- Chronic Myeloid Leukemia Treatments 3
- Oncology 8
- Co-authors
- James Heilman (1 shared paper)Michaël R. Laurent (1 shared paper)Samir C. Grover (1 shared paper)Daniel J. Lodge (1 shared paper)G. M. Beards (1 shared paper)Michael Bonert (1 shared paper)Bertalan Meskó (1 shared paper)Tim J. Vickers (1 shared paper)
- Journals
- Blood (6 papers)Bone Marrow Transplantation (2 papers)Journal of Clinical Oncology (2 papers)The Oncologist (1 paper)Journal of Medical Internet Research (1 paper)
- Partner nations
- United StatesCanadaVietnam
In The Last Decade
David Iberri
21 papers receiving 305 citations
Peers
Comparison fields: 5 of 62
- Communication 94
- Hematology 61
- Health 41
- Genetics 29
- Oncology 65
Countries citing papers authored by David Iberri
This map shows the geographic impact of David Iberri'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 David Iberri with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Iberri more than expected).
Fields of papers citing papers by David Iberri
This network shows the impact of papers produced by David Iberri. 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 David Iberri. The network helps show where David Iberri may publish in the future.
Co-authors
The 25 scholars most cited alongside David Iberri, 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 25 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2011 | 164 | |
| 2 | 2019 | 54 | |
| 3 | 2020 | 18 | |
| 4 | 2021 | 11 | |
| 5 | 2020 | 8 | |
| 6 | 2015 | 8 | |
| 7 | 2015 | 7 | |
| 8 | 2012 | 7 | |
| 9 | 2015 | 5 | |
| 10 | 2021 | 5 | |
| 11 | 2015 | 4 | |
| 12 | 2018 | 4 | |
| 13 | 2024 | 3 | |
| 14 | 2014 | 3 | |
| 15 | 2025 | 3 | |
| 16 | Machine Learning Predictability of Clinical Next Generation Sequencing for Hematologic Malignancies to Guide High-Value Precision Medicine. | 2021 | 3 |
| 17 | 2020 | 2 | |
| 18 | 2024 | 2 | |
| 19 | 2021 | 1 | |
| 20 | 2020 | 1 |
About David Iberri
David Iberri is a scholar working on Hematology, Oncology, Molecular Biology, Genetics and Pathology and Forensic Medicine, having authored 25 papers that have together received 314 indexed citations. Recurring topics across this work include Multiple Myeloma Research and Treatments (8 papers), Chronic Lymphocytic Leukemia Research (5 papers), Cancer Genomics and Diagnostics (3 papers), Amyloidosis: Diagnosis, Treatment, Outcomes (3 papers), Chronic Myeloid Leukemia Treatments (3 papers), Lymphoma Diagnosis and Treatment (3 papers), Social Media in Health Education (3 papers) and Protein Degradation and Inhibitors (2 papers). The work is most often cited by research in Communication (94 citations), Hematology (61 citations), Health (41 citations), Genetics (29 citations) and Oncology (65 citations). David Iberri has collaborated with scholars based in United States, Canada and Vietnam. Frequent co-authors include James Heilman, Michaël R. Laurent, Samir C. Grover, Daniel J. Lodge, G. M. Beards, Michael Bonert, Bertalan Meskó, Tim J. Vickers, Wouter Stomp and Jacob F de Wolff. Their work appears in journals such as Blood, Bone Marrow Transplantation, Journal of Clinical Oncology, The Oncologist and Journal of Medical Internet 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.