J. Haessler

1.0k total citations
7 papers, 147 citations indexed

About

J. Haessler is a scholar working on Molecular Biology, Hematology and Oncology. According to data from OpenAlex, J. Haessler has authored 7 papers receiving a total of 147 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Molecular Biology, 5 papers in Hematology and 3 papers in Oncology. Recurrent topics in J. Haessler's work include Multiple Myeloma Research and Treatments (5 papers), Protein Degradation and Inhibitors (4 papers) and HIV/AIDS drug development and treatment (2 papers). J. Haessler is often cited by papers focused on Multiple Myeloma Research and Treatments (5 papers), Protein Degradation and Inhibitors (4 papers) and HIV/AIDS drug development and treatment (2 papers). J. Haessler collaborates with scholars based in United States, United Kingdom and France. J. Haessler's co-authors include Bart Barlogie, John Crowley, Brian G.M. Durie, Gareth J. Morgan, Rafaël Fonseca, F. Ross, S. Vincent Rajkumar, Hervé Avet‐Loiseau, Orhan Sezer and Juan José Lahuerta and has published in prestigious journals such as Journal of Clinical Oncology and Leukemia.

In The Last Decade

J. Haessler

6 papers receiving 144 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
J. Haessler United States	4	140	116	80	14	5	7	147
José-María Arguiñano Spain	2	125 0.9×	104 0.9×	55 0.7×	15 1.1×	3 0.6×	4	128
Christopher Maisel United States	7	89 0.6×	79 0.7×	53 0.7×	15 1.1×	8 1.6×	20	127
Rafael Martínez-Martinez Mexico	4	191 1.4×	181 1.6×	118 1.5×	14 1.0×	12 2.4×	6	236
C. Hulin France	6	161 1.1×	141 1.2×	92 1.1×	20 1.4×	19 3.8×	16	188
Sandra Wear United States	7	236 1.7×	245 2.1×	107 1.3×	20 1.4×	4 0.8×	7	276
Ana-Isabel Teruel Spain	3	175 1.3×	140 1.2×	84 1.1×	26 1.9×	4 0.8×	3	181
Karin H. Herbschleb United Kingdom	6	46 0.3×	126 1.1×	79 1.0×	13 0.9×	8 1.6×	10	173
S Caltagirone Italy	5	230 1.6×	219 1.9×	123 1.5×	15 1.1×	8 1.6×	11	251
Rianne Ammerlaan Netherlands	4	294 2.1×	247 2.1×	169 2.1×	21 1.5×	4 0.8×	6	302
Irene Aires‐Mejía Spain	2	99 0.7×	68 0.6×	59 0.7×	25 1.8×	15 3.0×	2	130

Countries citing papers authored by J. Haessler

Since Specialization

Citations

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

Fields of papers citing papers by J. Haessler

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of J. Haessler

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

All Works

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7 of 7 papers shown

Avet‐Loiseau, Hervé, Brian G.M. Durie, Michèle Cavo, et al.. (2012). Combining fluorescent in situ hybridization data with ISS staging improves risk assessment in myeloma: an International Myeloma Working Group collaborative project. Leukemia. 27(3). 711–717. 126 indexed citations

Shaughnessy, John D., Pingping Qu, Ricky D. Edmondson, et al.. (2010). Outcome with total therapy 3 (TT3) compared to total therapy 2 (TT2): Role of GEP70-defined high-risk disease with trisomy of 1q21 and activation of the proteasome gene PSMD4.. Journal of Clinical Oncology. 28(15_suppl). 8027–8027. 2 indexed citations

Sawyer, Jeffery R., John D. Shaughnessy, J. Haessler, Antje Hoering, & Bart Barlogie. (2010). Gene expression profiling (GEP) in multiple myeloma (MM): Distinguishing relapses with high-risk transformation from those with sustained low risk.. Journal of Clinical Oncology. 28(15_suppl). 8122–8122. 1 indexed citations

Durie, Brian G.M., Brian Van Ness, Owen Stephens, et al.. (2009). Genetic polymorphisms of EPHX1, Gsk3β, TNFSF8 and myeloma cell DKK-1 expression linked to bone disease in myeloma. Leukemia. 23(10). 1913–1919. 10 indexed citations

Shaughnessy, John D., et al.. (2009). Use of bortezomib (BOR) pharmacogenomics (PG) to identify mechanisms of drug resistance and predict survival in multiple myeloma (MM) treated with total therapy 3 (TT3). Journal of Clinical Oncology. 27(15_suppl). 8538–8538.

Barlogie, Bart, E. Anaissie, Frits van Rhee, et al.. (2008). Total therapy (TT) for myeloma (MM)—10% cure rate with TT1 suggested by >10yr continuous complete remission (CCR): Bortezomib in TT3 overcomes poor-risk associated with T(4;14) and DelTP53 in TT2. Journal of Clinical Oncology. 26(15_suppl). 8516–8516. 5 indexed citations

Pineda‐Roman, Mauricio, J. Haessler, Klaus Hollmig, et al.. (2007). High-dose melphalan (MEL) based autotransplants (AT) for multiple myeloma (MM): The Arkansas experience since 1989 in more than 2,800 patients. Journal of Clinical Oncology. 25(18_suppl). 8043–8043. 3 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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