Shimin Hu

10.7k total citations
160 papers, 4.1k citations indexed

About

Shimin Hu is a scholar working on Hematology, Genetics and Pathology and Forensic Medicine. According to data from OpenAlex, Shimin Hu has authored 160 papers receiving a total of 4.1k indexed citations (citations by other indexed papers that have themselves been cited), including 74 papers in Hematology, 70 papers in Genetics and 57 papers in Pathology and Forensic Medicine. Recurrent topics in Shimin Hu's work include Lymphoma Diagnosis and Treatment (55 papers), Acute Myeloid Leukemia Research (51 papers) and Chronic Lymphocytic Leukemia Research (49 papers). Shimin Hu is often cited by papers focused on Lymphoma Diagnosis and Treatment (55 papers), Acute Myeloid Leukemia Research (51 papers) and Chronic Lymphocytic Leukemia Research (49 papers). Shimin Hu collaborates with scholars based in United States, China and Spain. Shimin Hu's co-authors include Xiaolu Yang, Claudius Vincenz, Vishva M. Dixit, L. Jeffrey Medeiros, Jian Ni, Guilin Tang, Mark Buller, Reiner Gentz, William F. Benedict and Wei Wang and has published in prestigious journals such as Cell, Journal of Biological Chemistry and Journal of Clinical Investigation.

In The Last Decade

Shimin Hu

148 papers receiving 4.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Shimin Hu United States 32 1.7k 1.3k 909 865 857 160 4.1k
Prasad Koduru United States 25 1.8k 1.0× 1.4k 1.1× 595 0.7× 952 1.1× 1.0k 1.2× 134 4.5k
Sandeep S. Davé United States 28 1.8k 1.0× 1.5k 1.2× 993 1.1× 1.3k 1.5× 644 0.8× 93 4.0k
Mathijs Baens Belgium 29 1.5k 0.9× 959 0.7× 1.2k 1.3× 842 1.0× 595 0.7× 51 3.7k
Hanno Glimm Germany 36 3.0k 1.8× 1.5k 1.2× 704 0.8× 298 0.3× 533 0.6× 124 4.9k
Roderick A.F. MacLeod Germany 34 2.3k 1.3× 843 0.6× 799 0.9× 592 0.7× 415 0.5× 128 4.0k
Elizabeth Hyjek United States 29 1.1k 0.6× 1.8k 1.3× 799 0.9× 671 0.8× 293 0.3× 68 3.6k
Katsuto Takenaka Japan 32 1.5k 0.9× 1.4k 1.1× 1.7k 1.9× 321 0.4× 725 0.8× 153 4.8k
Pier Paolo Piccaluga Italy 40 1.3k 0.7× 2.1k 1.6× 1.4k 1.6× 2.7k 3.1× 1.2k 1.4× 208 5.8k
Hideki Asaoku Japan 28 1.4k 0.8× 1.6k 1.2× 882 1.0× 703 0.8× 549 0.6× 102 3.9k
Massimo Geuna Italy 32 1.7k 1.0× 1.1k 0.8× 1.5k 1.7× 983 1.1× 1.1k 1.2× 84 4.4k

Countries citing papers authored by Shimin Hu

Since Specialization
Citations

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

Fields of papers citing papers by Shimin Hu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shimin Hu

This figure shows the co-authorship network connecting the top 25 collaborators of Shimin Hu. A scholar is included among the top collaborators of Shimin Hu 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 Shimin Hu. Shimin Hu 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.
2.
Hu, Shimin, Jie Xu, Andres Quesada, et al.. (2025). 1308 The Value of Optical Genome Mapping in Diagnostically Challenging Cases of Low-Grade B-Cell Lymphoma. Laboratory Investigation. 105(3). 103544–103544.
3.
Wei, Qing, Shimin Hu, Sanam Loghavi, et al.. (2025). Chromoanagenesis Is Frequently Associated With Highly Complex Karyotypes, Extensive Clonal Heterogeneity, and Treatment Refractoriness in Acute Myeloid Leukemia. American Journal of Hematology. 100(3). 417–426. 6 indexed citations
4.
Tang, Guilin, et al.. (2025). 1307 Mixed-Phenotype Acute Leukemia with TP53 Mutation: A Study of 28 Patients. Laboratory Investigation. 105(3). 103543–103543.
5.
Shuyu, E, Francisco Vega, Hong Fang, et al.. (2025). BCL11B helps to define T-lineage in lymphomas/leukaemias with a mixed/ambiguous immunophenotype. Pathology. 57(6). 746–752. 1 indexed citations
6.
Tang, Guilin, Preetesh Jain, Shimin Hu, et al.. (2025). Optical genome mapping reveals diverse mechanisms of cyclin activation in mantle cell lymphomas lacking IGH::CCND1. Human Pathology. 159. 105793–105793. 2 indexed citations
7.
Hu, Shimin, Elias Jabbour, Guilin Tang, et al.. (2024). Recurrent lymphoid and myeloid relapses due to treatment cessations reveal natural history of Ph‐positive B‐ALL and pose a diagnostic challenge. American Journal of Hematology. 99(4). 721–726.
8.
9.
Fang, Hong, Sa A. Wang, Hannah C. Beird, et al.. (2024). Morphology, immunophenotype, and suggested diagnostic criteria of TCL1 family–negative T-prolymphocytic leukemia. American Journal of Clinical Pathology. 162(6). 582–590. 1 indexed citations
10.
Loghavi, Sanam, Qing Wei, Farhad Ravandi, et al.. (2024). Optical genome mapping improves the accuracy of classification, risk stratification, and personalized treatment strategies for patients with acute myeloid leukemia. American Journal of Hematology. 99(10). 1959–1968. 17 indexed citations
11.
Jiang, Chen, Liyun Huang, Jihao Zhou, et al.. (2023). Epstein-Barr virus-based prognostic model in nodular sclerosis classic Hodgkin lymphoma. iScience. 27(1). 108630–108630.
12.
Wang, Sa A., Jeffrey L. Jorgensen, Shimin Hu, et al.. (2023). Validation of a 12‐color flow cytometry assay for acute myeloid leukemia minimal/measurable residual disease detection. Cytometry Part B Clinical Cytometry. 104(5). 356–366. 10 indexed citations
13.
Khoury, Joseph D., Mark J. Routbort, Keyur P. Patel, et al.. (2022). Clinicopathologic spectrum of myeloid neoplasms with concurrent myeloproliferative neoplasm driver mutations and SRSF2 mutations. Modern Pathology. 35(11). 1677–1683. 3 indexed citations
14.
Loghavi, Sanam, Dawen Sui, Peng Wei, et al.. (2018). Validation of the 2017 revision of the WHO chronic myelomonocytic leukemia categories. Blood Advances. 2(15). 1807–1816. 29 indexed citations
15.
Gong, Zimu, L. Jeffrey Medeiros, Jörge E. Cortes, et al.. (2017). Cytogenetics-Based Risk Prediction of Blastic Transformation of CML Treated with Tyrosine Kinase Inhibitors. Blood. 130. 247–247.
16.
Wang, Wei, Jörge E. Cortes, Guilin Tang, et al.. (2016). Risk stratification of chromosomal abnormalities in chronic myelogenous leukemia in the era of tyrosine kinase inhibitor therapy. Blood. 127(22). 2742–2750. 111 indexed citations
17.
Tang, Zhenya, Yan Li, Sa A. Wang, et al.. (2016). Clinical significance of acquired loss of the X chromosome in bone marrow. Leukemia Research. 47. 109–113. 8 indexed citations
18.
Oo, Thein Hlaing & Shimin Hu. (2016). Copper deficiency‐related bone marrow changes secondary to long‐term total parenteral nutrition. Clinical Case Reports. 5(2). 195–196. 6 indexed citations
19.
Hu, Shimin, et al.. (2006). CIAP2 Inhibits Anigen Receptor Signaling by Targeting Bcl10 for Degredation. Cell Cycle. 5(13). 1438–1442. 18 indexed citations
20.
Hu, Shimin. (2005). cIAP2 is a ubiquitin protein ligase for BCL10 and is dysregulated in mucosa-associated lymphoid tissue lymphomas. Journal of Clinical Investigation. 116(1). 174–181. 86 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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