Julia Krzykalla

1.4k total citations
17 papers, 195 citations indexed

About

Julia Krzykalla is a scholar working on Hematology, Molecular Biology and Genetics. According to data from OpenAlex, Julia Krzykalla has authored 17 papers receiving a total of 195 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Hematology, 7 papers in Molecular Biology and 5 papers in Genetics. Recurrent topics in Julia Krzykalla's work include Acute Myeloid Leukemia Research (5 papers), Multiple Myeloma Research and Treatments (3 papers) and Epigenetics and DNA Methylation (3 papers). Julia Krzykalla is often cited by papers focused on Acute Myeloid Leukemia Research (5 papers), Multiple Myeloma Research and Treatments (3 papers) and Epigenetics and DNA Methylation (3 papers). Julia Krzykalla collaborates with scholars based in Germany, United States and Italy. Julia Krzykalla's co-authors include Axel Benner, Peter Dreger, Thomas Luft, Nicholas Schreck, Magnus von Knebel Doeberitz, Carsten Müller‐Tidow, Matthias Kloor, Martin Schneider, Lars Bullinger and Niels Grabe and has published in prestigious journals such as Journal of Clinical Oncology, Blood and International Journal of Cancer.

In The Last Decade

Julia Krzykalla

17 papers receiving 193 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Julia Krzykalla Germany 8 93 63 54 54 26 17 195
Allison Winter United States 8 117 1.3× 53 0.8× 41 0.8× 100 1.9× 13 0.5× 39 221
Konstantinos Christofyllakis Germany 6 64 0.7× 26 0.4× 32 0.6× 45 0.8× 24 0.9× 30 149
James E. Radford United States 6 84 0.9× 90 1.4× 30 0.6× 42 0.8× 6 0.2× 8 209
Sarah Reinke Germany 8 69 0.7× 15 0.2× 20 0.4× 92 1.7× 23 0.9× 22 154
Barbara Sosnowska‐Pasiarska Poland 8 94 1.0× 27 0.4× 22 0.4× 31 0.6× 9 0.3× 18 201
Jie Zi China 10 39 0.4× 65 1.0× 100 1.9× 20 0.4× 29 1.1× 32 256
Amy D Greenway United States 4 94 1.0× 42 0.7× 26 0.5× 32 0.6× 10 0.4× 9 165
Miriam Schulz Germany 7 93 1.0× 63 1.0× 29 0.5× 39 0.7× 6 0.2× 7 209
Yasir Khan United States 9 62 0.7× 20 0.3× 35 0.6× 29 0.5× 12 0.5× 24 211
Sébastien Lachot France 5 36 0.4× 87 1.4× 53 1.0× 12 0.2× 26 1.0× 12 169

Countries citing papers authored by Julia Krzykalla

Since Specialization
Citations

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

Fields of papers citing papers by Julia Krzykalla

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Julia Krzykalla

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

All Works

17 of 17 papers shown
1.
2.
Jahn, Nikolaus, Maral Saadati, Pierre Fenaux, et al.. (2023). Clinical impact of the genomic landscape and leukemogenic trajectories in non-intensively treated elderly acute myeloid leukemia patients. Leukemia. 37(11). 2187–2196. 22 indexed citations
3.
Jahn, Nikolaus, Maral Saadati, Pierre Fenaux, et al.. (2023). P417: GENOMIC LANDSCAPE AND PROGNOSIS IN OLDER ACUTE MYELOID LEUKEMIA PATIENTS NOT ELIGIBLE FOR INTENSIVE CHEMOTHERAPY. HemaSphere. 7(S3). e09006a7–e09006a7. 1 indexed citations
4.
Korell, Felix, Olaf Penack, Mike Mattie, et al.. (2022). EASIX and Severe Endothelial Complications After CD19-Directed CAR-T Cell Therapy—A Cohort Study. Frontiers in Immunology. 13. 877477–877477. 32 indexed citations
5.
Krzykalla, Julia, Silke Kapp‐Schwoerer, Verena I. Gaidzik, et al.. (2021). Cluster of differentiation 33 single nucleotide polymorphism rs12459419 is a predictive factor in patients with <i>nucleophosmin1</i>-mutated acute myeloid leukemia receiving gemtuzumab ozogamicin. Haematologica. 106(11). 2986–2989. 5 indexed citations
6.
Bloehdorn, Johannes, Julia Krzykalla, Karlheinz Holzmann, et al.. (2021). Integrative prognostic models predict long-term survival after immunochemotherapy in chronic lymphocytic leukemia patients. Haematologica. 107(3). 615–624. 2 indexed citations
7.
Basset, Marco, Christoph Kimmich, Nicholas Schreck, et al.. (2021). Lenalidomide and dexamethasone in relapsed/refractory immunoglobulin light chain (AL) amyloidosis: results from a large cohort of patients with long follow‐up. British Journal of Haematology. 195(2). 230–243. 11 indexed citations
8.
Broeke, Sanne W. ten, Alexej Ballhausen, Aysel Ahadova, et al.. (2021). The coding microsatellite mutation profile of PMS2-deficient colorectal cancer. Experimental and Molecular Pathology. 122. 104668–104668. 8 indexed citations
9.
Jung, Audrey, Anika Hüsing, Sabine Behrens, et al.. (2020). Postdiagnosis weight change is associated with poorer survival in breast cancer survivors: A prospective population‐based patient cohort study. International Journal of Cancer. 148(1). 18–27. 12 indexed citations
10.
Radujkovic, Aleksandar, Lambros Kordelas, Julia Krzykalla, et al.. (2019). Pre-transplant testosterone and outcome of men after allogeneic stem cell transplantation. Haematologica. 105(5). 1454–1464. 1 indexed citations
11.
Krzykalla, Julia, Axel Benner, & Annette Kopp‐Schneider. (2019). Exploratory identification of predictive biomarkers in randomized trials with normal endpoints. Statistics in Medicine. 39(7). 923–939. 7 indexed citations
12.
Krzykalla, Julia, et al.. (2017). Fokale Läsionen in der Ganzkörper-MRT beim multiplen Myelom. Der Radiologe. 58(1). 72–78. 2 indexed citations
13.
Käyser, Sabine, Julia Krzykalla, Matthias Schick, et al.. (2017). Clinical impact ofKMT2C andSPRY4 expression levels in intensively treated younger adult acute myeloid leukemia patients. European Journal Of Haematology. 99(6). 544–552. 5 indexed citations
14.
Müller, Meike, Julia Krzykalla, Fabian Echterdiek, et al.. (2017). High numbers of PDCD1 (PD-1)-positive T cells andB2Mmutations in microsatellite-unstable colorectal cancer. OncoImmunology. 7(2). e1390640–e1390640. 43 indexed citations
15.
Radujkovic, Aleksandar, Lambros Kordelas, Julia Krzykalla, et al.. (2017). Pretransplant Vitamin D Deficiency Is Associated With Higher Relapse Rates in Patients Allografted for Myeloid Malignancies. Journal of Clinical Oncology. 35(27). 3143–3152. 27 indexed citations
16.
Gross, J., Johanna Nattenmüller, Diana Tichy, et al.. (2017). Body fat composition as predictive factor for treatment response in patients with newly diagnosed multiple myeloma - subgroup analysis of the prospective GMMG MM5 trial. Oncotarget. 8(40). 68460–68471. 15 indexed citations
17.
Käyser, Sabine, Julia Krzykalla, Matthias Schick, et al.. (2016). Clinical Impact of KMT2C and SPRY4 Expression Levels in Intensively Treated Younger Adult Acute Myeloid Leukemia Patients. Blood. 128(22). 1663–1663. 1 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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