T. Haferlach

617 total citations
10 papers, 283 citations indexed

About

T. Haferlach is a scholar working on Molecular Biology, Hematology and Genetics. According to data from OpenAlex, T. Haferlach has authored 10 papers receiving a total of 283 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Molecular Biology, 6 papers in Hematology and 4 papers in Genetics. Recurrent topics in T. Haferlach's work include Acute Myeloid Leukemia Research (4 papers), Myeloproliferative Neoplasms: Diagnosis and Treatment (3 papers) and T-cell and Retrovirus Studies (1 paper). T. Haferlach is often cited by papers focused on Acute Myeloid Leukemia Research (4 papers), Myeloproliferative Neoplasms: Diagnosis and Treatment (3 papers) and T-cell and Retrovirus Studies (1 paper). T. Haferlach collaborates with scholars based in Germany, United States and Netherlands. T. Haferlach's co-authors include Wolfgang Kern, Tamara Alpermann, Ulrike Bacher, Claudia Haferlach, Susanne Schnittger, S. Schnittger, Christiane Eder, Niroshan Nadarajah, Claudia Haferlach and Norbert Schmitz and has published in prestigious journals such as Journal of Clinical Oncology, British Journal of Haematology and Leukemia.

In The Last Decade

T. Haferlach

9 papers receiving 276 citations

Peers

T. Haferlach

HEMAT

MB

GENET

PFM

ONCOL

Laurence Baranger France

Susanna Akiki Qatar

Camino Estivill Spain

Susana Bizarro Portugal

Cecilia Arana Yi United States

Borhane Slama France

Kaddour Chabane France

Sarah Abu Kar United States

Dominique Penther France

Jacqueline Maaloufová Czechia

Laurence Baranger France

T. Haferlach

244 ×1.2

HEMAT

110 ×0.8

MB

104 ×0.9

GENET

63 ×1.1

PFM

35 ×0.8

ONCOL

Citations per year, relative to T. Haferlach T. Haferlach (= 1×) peers Laurence Baranger

Countries citing papers authored by T. Haferlach

Since Specialization

Citations

This map shows the geographic impact of T. Haferlach'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 T. Haferlach with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites T. Haferlach more than expected).

Fields of papers citing papers by T. Haferlach

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of T. Haferlach

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

All Works

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10 of 10 papers shown

1.

Eckardt, Jan‐Niklas, Wencke Walter, Jan Moritz Middeke, & T. Haferlach. (2025). Artificial intelligence for risk assessment and outcome prediction in malignant haematology. British Journal of Haematology. 208(1). 25–38.

2.

Eder, Christiane, Tamara Alpermann, Ulrike Bacher, et al.. (2014). High number of additional genetic lesions in acute myeloid leukemia with t(8;21)/RUNX1-RUNX1T1: frequency and impact on clinical outcome. Leukemia. 28(7). 1449–1458. 93 indexed citations

3.

Kern, Wolfgang, Claudia Haferlach, S. Schnittger, Tamara Alpermann, & T. Haferlach. (2012). Serial assessment of suspected myelodysplastic syndromes: significance of flow cytometric findings validated by cytomorphology, cytogenetics, and molecular genetics. Haematologica. 98(2). 201–207. 26 indexed citations

4.

Schnittger, S., Ulrike Bacher, Claudia Haferlach, et al.. (2011). Clinical impact of FLT3 mutation load in acute promyelocytic leukemia with t(15;17)/PML-RARA. Haematologica. 96(12). 1799–1807. 42 indexed citations

5.

Schnittger, Susanne, Ulrike Bacher, Claudia Haferlach, et al.. (2009). Detection of JAK2 exon 12 mutations in 15 patients with JAK2V617F negative polycythemia vera. Haematologica. 94(3). 414–418. 47 indexed citations

6.

Berg, Tobias, Manfred Fliegauf, Jan A. Burger, et al.. (2008). Transcriptional upregulation of p21/WAF/Cip1 in myeloid leukemic blasts expressing AML1-ETO. Haematologica. 93(11). 1728–1733. 19 indexed citations

7.

Schlegelberger, Brigitte, T. Zwingers, Karin Hohenadel, et al.. (1996). Significance of cytogenetic findings for the clinical outcome in patients with T-cell lymphoma of angioimmunoblastic lymphadenopathy type.. Journal of Clinical Oncology. 14(2). 593–599. 41 indexed citations

8.

Wichmann, Manfred, et al.. (1996). Can Blood Group 0 Red Cells of Donor Origin Acquire Weak Group A Reactivity through Serum A Transferase of the Recipient after Bone Marrow Transplantation?. Transfusion Medicine and Hemotherapy. 23(1). 29–31. 2 indexed citations

9.

Dreger, Peter, T. Haferlach, S. Jacobs, et al.. (1994). Peripheral blood progenitor cell but not bone marrow grafts are affected by previous Dexa-BEAM chemotherapy. 145. 1 indexed citations

10.

Schmitz, Norbert, T. Haferlach, H Griesser, et al.. (1991). High-dose chemotherapy and autologous bone marrow transplantation in relapsing angioimmunoblastic lymphadenopathy with dysproteinemia (AILD).. PubMed. 8(6). 503–6. 12 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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