Rafael Bejar

15.3k total citations · 3 hit papers
94 papers, 5.9k citations indexed

About

Rafael Bejar is a scholar working on Hematology, Genetics and Molecular Biology. According to data from OpenAlex, Rafael Bejar has authored 94 papers receiving a total of 5.9k indexed citations (citations by other indexed papers that have themselves been cited), including 70 papers in Hematology, 37 papers in Genetics and 33 papers in Molecular Biology. Recurrent topics in Rafael Bejar's work include Acute Myeloid Leukemia Research (69 papers), Myeloproliferative Neoplasms: Diagnosis and Treatment (24 papers) and Cancer Genomics and Diagnostics (19 papers). Rafael Bejar is often cited by papers focused on Acute Myeloid Leukemia Research (69 papers), Myeloproliferative Neoplasms: Diagnosis and Treatment (24 papers) and Cancer Genomics and Diagnostics (19 papers). Rafael Bejar collaborates with scholars based in United States, Germany and Spain. Rafael Bejar's co-authors include Benjamin L. Ebert, David P. Steensma, R. Coleman Lindsley, Ross L. Levine, Mikkael A. Sekeres, Robert P. Hasserjian, Siddhartha Jaiswal, Donna Neuberg, Kristen E. Stevenson and Guillermo Garcia‐Manero and has published in prestigious journals such as New England Journal of Medicine, Proceedings of the National Academy of Sciences and Journal of Clinical Investigation.

In The Last Decade

Rafael Bejar

85 papers receiving 5.8k citations

Hit Papers

Clonal hematopoiesis of indeterminate potential and its d... 2011 2026 2016 2021 2015 2011 2014 400 800 1.2k

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Rafael Bejar United States 29 4.2k 2.3k 2.0k 1.1k 602 94 5.9k
Rashmi Kanagal‐Shamanna United States 34 1.9k 0.5× 1.2k 0.5× 1.3k 0.7× 825 0.7× 406 0.7× 223 4.0k
Antonio Cuneo Italy 41 1.9k 0.4× 1.5k 0.6× 2.2k 1.1× 438 0.4× 1.1k 1.9× 243 5.1k
Adam J. Mead United Kingdom 34 2.5k 0.6× 2.0k 0.9× 1.7k 0.8× 502 0.4× 604 1.0× 126 4.2k
Robert Månsson Sweden 34 2.2k 0.5× 2.6k 1.1× 759 0.4× 401 0.4× 2.5k 4.2× 73 5.6k
Mark J. Routbort United States 41 1.3k 0.3× 1.6k 0.7× 1.0k 0.5× 1.8k 1.6× 270 0.4× 162 5.4k
Mingjiang Xu United States 34 1.7k 0.4× 3.0k 1.3× 1.5k 0.7× 499 0.4× 395 0.7× 101 4.4k
Ingmar Bruns Germany 28 2.5k 0.6× 1.1k 0.4× 1.1k 0.5× 238 0.2× 969 1.6× 80 3.8k
Anna Savoia Italy 38 1.7k 0.4× 1.7k 0.7× 533 0.3× 332 0.3× 406 0.7× 121 4.1k
C. Glenn Begley Australia 33 1.9k 0.5× 2.0k 0.9× 610 0.3× 253 0.2× 1.0k 1.7× 73 4.5k
Koji Shido United States 25 1.5k 0.4× 2.4k 1.0× 810 0.4× 668 0.6× 959 1.6× 32 5.5k

Countries citing papers authored by Rafael Bejar

Since Specialization
Citations

This map shows the geographic impact of Rafael Bejar'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 Rafael Bejar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rafael Bejar more than expected).

Fields of papers citing papers by Rafael Bejar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Rafael Bejar. 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 Rafael Bejar. The network helps show where Rafael Bejar may publish in the future.

Co-authorship network of co-authors of Rafael Bejar

This figure shows the co-authorship network connecting the top 25 collaborators of Rafael Bejar. A scholar is included among the top collaborators of Rafael Bejar 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 Rafael Bejar. Rafael Bejar is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Borrat, X., et al.. (2025). From Admission to Discharge: Leveraging NLP for Upstream Primary Coding with SNOMED CT. Journal of Medical Systems. 49(1). 68–68.
2.
Jonas, Brian A., Peter Curtin, Gary J. Schiller, et al.. (2025). A phase 1 trial of ibrutinib and azacitidine for higher risk myelodysplastic syndromes (University of California Hematologic Malignancies Consortium Study 1503). Leukemia Research. 155. 107717–107717.
4.
Sonowal, Himangshu, William G. Rice, Rafael Bejar, et al.. (2024). Preclinical Development of Tuspetinib for the Treatment of Acute Myeloid Leukemia. Cancer Research Communications. 5(1). 74–83. 3 indexed citations
5.
Ferrall‐Fairbanks, Meghan C., Brian Johnson, Christopher T. Letson, et al.. (2022). Progenitor Hierarchy of Chronic Myelomonocytic Leukemia Identifies Inflammatory Monocytic-Biased Trajectory Linked to Worse Outcomes. Blood Cancer Discovery. 3(6). 536–553. 9 indexed citations
6.
Park, Soohyung, Chan‐Wang Jerry Lio, Edahí González‐Avalos, et al.. (2020). 5-Azacytidine Transiently Restores Dysregulated Erythroid Differentiation Gene Expression in TET2-Deficient Erythroleukemia Cells. Molecular Cancer Research. 19(3). 451–464. 4 indexed citations
7.
Tarke, Alison, Luca Ferrari, Franco Ferrari, et al.. (2020). In vitro induction of neoantigen-specific T cells in myelodysplastic syndrome, a disease with low mutational burden. Cytotherapy. 23(4). 320–328. 8 indexed citations
9.
Adelman, Emmalee R., Alejandro Roisman, André Olsson, et al.. (2019). Aging Human Hematopoietic Stem Cells Manifest Profound Epigenetic Reprogramming of Enhancers That May Predispose to Leukemia. Cancer Discovery. 9(8). 1080–1101. 136 indexed citations
10.
Stoner, Samuel A., Ming Yan, Katherine Liu, et al.. (2019). Hippo kinase loss contributes to del(20q) hematologic malignancies through chronic innate immune activation. Blood. 134(20). 1730–1744. 12 indexed citations
11.
Zhang, Ling, Donald M. Stablein, Pearlie K. Epling‐Burnette, et al.. (2018). Diagnosis of Myelodysplastic Syndromes and Related Conditions: Rates of Discordance between Local and Central Review in the NHLBI MDS Natural History Study. Blood. 132(Supplement 1). 4370–4370. 3 indexed citations
12.
Ferrari, Valentina, Tiffany Tanaka, Alison Tarke, et al.. (2018). A Phase 1 Clinical Trial of Personalized Adoptive Cellular Therapy Targeting Myelodysplastic Syndrome (MDS) Stem Cell Neoantigens (PACTN). Blood. 132(Supplement 1). 4373–4373. 1 indexed citations
13.
Dow, Michelle T., et al.. (2018). The Emerging Potential for Network Analysis to Inform Precision Cancer Medicine. Journal of Molecular Biology. 430(18). 2875–2899. 53 indexed citations
14.
Bejar, Rafael. (2016). Implications of molecular genetic diversity in myelodysplastic syndromes. Current Opinion in Hematology. 24(2). 73–78. 35 indexed citations
15.
Pérez-Ladaga, Albert, Bennett Adam Caughey, Huafeng Xie, et al.. (2013). Functional Defects In Neutrophils Derived From Ezh2 Null Mice. Blood. 122(21). 1556–1556. 1 indexed citations
16.
Bejar, Rafael, Kristen E. Stevenson, Bennett Adam Caughey, et al.. (2012). Validation of a Prognostic Model and the Impact of Mutations in Patients With Lower-Risk Myelodysplastic Syndromes. Journal of Clinical Oncology. 30(27). 3376–3382. 312 indexed citations
17.
Alfonso, Israel, et al.. (1986). Spinal cord involvement in encephalocraniocutaneous lipomatosis. Pediatric Neurology. 2(6). 380–384. 24 indexed citations
18.
Duchowny, Michael, et al.. (1984). Hypothalamic mass and gigantism in neurofibromatosis: Treatment with bromocriptine. Annals of Neurology. 15(3). 302–304. 18 indexed citations
19.
Oberfield, Sharon E., Lenore S. Levine, Robert M. Carey, Rafael Bejar, & Maria I. New. (1979). Pseudohypoaldosteronism: Multiple Target Organ Unresponsiveness to Mineralocorticoid Hormones*. The Journal of Clinical Endocrinology & Metabolism. 48(2). 228–234. 76 indexed citations
20.
Baron, Michael, et al.. (1970). NEUROLOGIC MANIFESTATIONS OF THE BATTERED CHILD SYNDROME. PEDIATRICS. 45(6). 1003–1007. 13 indexed citations

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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