Daniel Afar
- Hematology top 0.5%
- Multiple Myeloma Research and Treatments 16
- Chronic Myeloid Leukemia Treatments 10
- Genetics top 1%
- Chronic Lymphocytic Leukemia Research 12
- Oncology top 2%
- HER2/EGFR in Cancer Research 7
- Immunology top 5%
- Molecular Biology top 5%
- Glycosylation and Glycoproteins Research 8
- Ubiquitin and proteasome pathways 6
-
- Monoclonal and Polyclonal Antibodies Research 17
-
- Lung Cancer Treatments and Mutations 5
- Co-authors
- Owen N. WitteAndrei GogaJami McLaughlinDouglas C. SaffranJohn C. BellAnil SinghalWilliam BensingerAnn Mohrbacher
- Cited by
- HematologyGeneticsOncology
- Journals
- Blood (13 papers)Journal of Clinical Oncology (7 papers)Molecular and Cellular Biology (7 papers)
- Partner nations
- United StatesCanadaFrance
In The Last Decade
Daniel Afar
67 papers receiving 3.9k citations
Peers
Comparison fields: 5 of 103
- Hematology 1.5k
- Genetics 822
- Oncology 1.1k
- Immunology 835
- Molecular Biology 2.0k
Countries citing papers authored by Daniel Afar
This map shows the geographic impact of Daniel Afar'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 Daniel Afar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Afar more than expected).
Fields of papers citing papers by Daniel Afar
This network shows the impact of papers produced by Daniel Afar. 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 Daniel Afar. The network helps show where Daniel Afar may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Daniel Afar, 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 | 2020 | 21 | |
| 2 | 2020 | 24 | |
| 3 | 2020 | 26 | |
| 4 | 2018 | 104 | |
| 5 | 2017 | 1 | |
| 6 | 2016 | 1 | |
| 7 | CS1 promotes multiple myeloma cell adhesion, clonogenic growth, and tumorigenicity via c-maf-mediated interactions with bone marrow stromal cells (Blood (2009) 113, 18, (4309-4318)) | 2010 | 5 |
| 8 | 2010 | 5 | |
| 9 | 2009 | 142 | |
| 10 | 2008 | 3 | |
| 11 | 2002 | 21 | |
| 12 | 1998 | 34 | |
| 13 | 1997 | 67 | |
| 14 | 1996 | 78 | |
| 15 | 1994 | 162 | |
| 16 | 1993 | 76 | |
| 17 | 1993 | 42 | |
| 18 | 1992 | 39 | |
| 19 | 1990 | 32 | |
| 20 | 1990 | 35 |
About Daniel Afar
Daniel Afar is a scholar working on Hematology, Genetics and Oncology, having authored 68 papers that have together received 4.0k indexed citations. Recurring topics across this work include Monoclonal and Polyclonal Antibodies Research (17 papers), Multiple Myeloma Research and Treatments (16 papers), Chronic Lymphocytic Leukemia Research (12 papers), Chronic Myeloid Leukemia Treatments (10 papers), Glycosylation and Glycoproteins Research (8 papers), HER2/EGFR in Cancer Research (7 papers), Ubiquitin and proteasome pathways (6 papers) and Lung Cancer Treatments and Mutations (5 papers). The work is most often cited by research in Hematology (1.5k citations), Genetics (822 citations) and Oncology (1.1k citations). Daniel Afar has collaborated with scholars based in United States, Canada and France. Frequent co-authors include Owen N. Witte, Andrei Goga, Jami McLaughlin, Douglas C. Saffran, John C. Bell, Owen N. Witte, Anil Singhal, William Bensinger, Ann Mohrbacher and James Kuo. Their work appears in journals such as Blood, Journal of Clinical Oncology, Molecular and Cellular Biology, Annals of Oncology and Proceedings of the National Academy of Sciences.
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.