Abeer Madbouly

1.2k total citations
34 papers, 855 citations indexed

About

Abeer Madbouly is a scholar working on Immunology, Hematology and Transplantation. According to data from OpenAlex, Abeer Madbouly has authored 34 papers receiving a total of 855 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Immunology, 16 papers in Hematology and 10 papers in Transplantation. Recurrent topics in Abeer Madbouly's work include T-cell and B-cell Immunology (20 papers), Hematopoietic Stem Cell Transplantation (15 papers) and Renal Transplantation Outcomes and Treatments (10 papers). Abeer Madbouly is often cited by papers focused on T-cell and B-cell Immunology (20 papers), Hematopoietic Stem Cell Transplantation (15 papers) and Renal Transplantation Outcomes and Treatments (10 papers). Abeer Madbouly collaborates with scholars based in United States, Germany and Israel. Abeer Madbouly's co-authors include Martin Maiers, Loren Gragert, John L. Freeman, Judith R. Kidd, Kenneth K. Kídd, Rixun Fang, Manohar R. Furtado, Mridu Middha, Françoise R. Friedlaender and A.J. Pakstis and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Kidney International.

In The Last Decade

Abeer Madbouly

32 papers receiving 836 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Abeer Madbouly United States 11 389 241 235 216 180 34 855
Suzanne Skoda‐Smith United States 13 579 1.5× 74 0.3× 117 0.5× 172 0.8× 394 2.2× 26 1.0k
Satoko Morishima Japan 21 665 1.7× 108 0.4× 86 0.4× 566 2.6× 207 1.1× 71 1.2k
Linda Stempora United States 20 625 1.6× 398 1.7× 74 0.3× 255 1.2× 291 1.6× 40 1.3k
Albert Naipal Netherlands 15 559 1.4× 38 0.2× 209 0.9× 152 0.7× 91 0.5× 25 859
S. Yoon Choo United States 12 518 1.3× 43 0.2× 90 0.4× 124 0.6× 103 0.6× 24 805
Katia Gagne France 21 1.0k 2.6× 97 0.4× 51 0.2× 365 1.7× 42 0.2× 57 1.2k
Claudia Sartirana Italy 10 903 2.3× 45 0.2× 143 0.6× 83 0.4× 104 0.6× 13 1.1k
A. Balas Spain 15 670 1.7× 91 0.4× 79 0.3× 293 1.4× 101 0.6× 180 1.1k
Hervé Betuel France 12 299 0.8× 78 0.3× 106 0.5× 48 0.2× 206 1.1× 23 764
Ji He China 13 711 1.8× 70 0.3× 89 0.4× 190 0.9× 142 0.8× 194 1.1k

Countries citing papers authored by Abeer Madbouly

Since Specialization
Citations

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

Fields of papers citing papers by Abeer Madbouly

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Abeer Madbouly

This figure shows the co-authorship network connecting the top 25 collaborators of Abeer Madbouly. A scholar is included among the top collaborators of Abeer Madbouly 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 Abeer Madbouly. Abeer Madbouly 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.
Maiers, Martin, Valerie S. Greco-Stewart, Abeer Madbouly, et al.. (2025). The Registry of Unmet Need: A World Marrow Donor Association Analysis of Patients Without an HLA Match. HLA. 105(5). e70255–e70255. 1 indexed citations
2.
Cerbo, Vincenzo Di, Hannah Song, Sean J. Hart, et al.. (2025). Artificial intelligence, machine learning, and digitalization systems in the cell and gene therapy sector: a guidance document from the ISCT industry committees. Cytotherapy. 27(8). 903–909. 1 indexed citations
3.
Spierings, Eric, et al.. (2024). Quantifying uncertainty of molecular mismatch introduced by mislabeled ancestry using haplotype-based HLA genotype imputation. Frontiers in Genetics. 15. 1444554–1444554. 6 indexed citations
4.
Damotte, Vincent, Chao Zhao, Eric Williams, et al.. (2024). Multiple measures for self-identification improve matching donors with patients in unrelated hematopoietic stem cell transplant. SHILAP Revista de lepidopterología. 4(1). 189–189. 4 indexed citations
5.
6.
Madbouly, Abeer, et al.. (2021). Predicting HLA-DPB1 permissive probabilities through a DPB1 prediction service towards the optimization of HCT donor selection. Human Immunology. 82(12). 903–911. 5 indexed citations
7.
Madbouly, Abeer, et al.. (2019). Single haplotype admixture models using large scale HLA genotype frequencies to reproduce human admixture. Immunogenetics. 71(10). 589–604. 2 indexed citations
8.
Sapir‐Pichhadze, Ruth, Xun Zhang, Abeer Madbouly, et al.. (2019). Epitopes as characterized by antibody-verified eplet mismatches determine risk of kidney transplant loss. Kidney International. 97(4). 778–785. 54 indexed citations
9.
10.
Valenzuela, Nicole M., Medhat Askar, Sebastiaan Heidt, et al.. (2018). Minimal data reporting standards for serological testing for histocompatibility. Human Immunology. 79(12). 865–868. 3 indexed citations
11.
Madbouly, Abeer, Tao Wang, Michael Haagenson, et al.. (2017). Investigating the Association of Genetic Admixture and Donor/Recipient Genetic Disparity with Transplant Outcomes. Biology of Blood and Marrow Transplantation. 23(6). 1029–1037. 8 indexed citations
12.
Manor, Sigal, et al.. (2016). High-resolution HLA A∼B∼DRB1 haplotype frequencies from the Ezer Mizion Bone Marrow Donor Registry in Israel. Human Immunology. 77(12). 1114–1119. 12 indexed citations
13.
14.
Dehn, Jason, Michelle Setterholm, Jane Kempenich, et al.. (2016). HapLogic: A Predictive Human Leukocyte Antigen–Matching Algorithm to Enhance Rapid Identification of the Optimal Unrelated Hematopoietic Stem Cell Sources for Transplantation. Biology of Blood and Marrow Transplantation. 22(11). 2038–2046. 57 indexed citations
15.
Madbouly, Abeer, Loren Gragert, John L. Freeman, et al.. (2014). Validation of statistical imputation of allele‐level multilocus phased genotypes from ambiguous HLA assignments. Tissue Antigens. 84(3). 285–292. 48 indexed citations
16.
Kídd, Kenneth K., William C. Speed, A.J. Pakstis, et al.. (2014). Progress toward an efficient panel of SNPs for ancestry inference. Forensic Science International Genetics. 10. 23–32. 200 indexed citations
17.
Gragert, Loren, Abeer Madbouly, John L. Freeman, & Martin Maiers. (2013). Six-locus high resolution HLA haplotype frequencies derived from mixed-resolution DNA typing for the entire US donor registry. Human Immunology. 74(10). 1313–1320. 286 indexed citations
18.
Schneider, Joel, Michael Heuer, Abeer Madbouly, et al.. (2012). TOOLS FOR IMPLEMENTATION OF SILVER STANDARD PRINCIPLES FOR HLA TYPING. UCL Discovery (University College London). 1 indexed citations
19.
Gragert, Loren, et al.. (2012). Measuring Ambiguity in HLA Typing Methods. PLoS ONE. 7(8). e43585–e43585. 20 indexed citations
20.
Maiers, Martin, Loren Gragert, Abeer Madbouly, et al.. (2012). 16th IHIW: Global analysis of registry HLA haplotypes from 20 Million individuals: Report from the IHIW Registry Diversity Group. International Journal of Immunogenetics. 40(1). 66–71. 18 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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