Julia E. Neumann

1.1k total citations
37 papers, 388 citations indexed

About

Julia E. Neumann is a scholar working on Molecular Biology, Genetics and Cancer Research. According to data from OpenAlex, Julia E. Neumann has authored 37 papers receiving a total of 388 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Molecular Biology, 11 papers in Genetics and 8 papers in Cancer Research. Recurrent topics in Julia E. Neumann's work include Glioma Diagnosis and Treatment (11 papers), Chromatin Remodeling and Cancer (8 papers) and Gene expression and cancer classification (4 papers). Julia E. Neumann is often cited by papers focused on Glioma Diagnosis and Treatment (11 papers), Chromatin Remodeling and Cancer (8 papers) and Gene expression and cancer classification (4 papers). Julia E. Neumann collaborates with scholars based in Germany, United States and Netherlands. Julia E. Neumann's co-authors include Ulrich Schüller, Mario M. Dorostkar, Christian Mawrin, Markus Glatzel, Armin Giese, Matthias Dottermusch, Christoph Harms, Thomas Mittlmeier, Marion Mühldorfer‐Fodor and Günther Kundt and has published in prestigious journals such as Nature Communications, SHILAP Revista de lepidopterología and ACS Nano.

In The Last Decade

Julia E. Neumann

34 papers receiving 386 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 E. Neumann Germany 13 182 131 86 70 51 37 388
David Mampre United States 11 154 0.8× 77 0.6× 77 0.9× 101 1.4× 53 1.0× 29 396
Edward J. Estlin United Kingdom 8 155 0.9× 97 0.7× 75 0.9× 61 0.9× 71 1.4× 10 380
Octavio Arevalo United States 13 170 0.9× 104 0.8× 85 1.0× 82 1.2× 138 2.7× 43 505
Jinfang Liu China 12 68 0.4× 178 1.4× 57 0.7× 46 0.7× 41 0.8× 35 447
Monika Hofer United Kingdom 12 59 0.3× 160 1.2× 161 1.9× 92 1.3× 47 0.9× 42 524
René Johannes Laursen Denmark 7 174 1.0× 95 0.7× 33 0.4× 63 0.9× 83 1.6× 15 385
Katharina Filipski Germany 12 203 1.1× 98 0.7× 25 0.3× 32 0.5× 55 1.1× 22 449
Jorge Villanúa Spain 15 150 0.8× 295 2.3× 62 0.7× 28 0.4× 48 0.9× 28 739
Azzam Ismail United Kingdom 12 90 0.5× 106 0.8× 72 0.8× 49 0.7× 57 1.1× 31 459
Guido Ahle France 13 223 1.2× 81 0.6× 270 3.1× 26 0.4× 49 1.0× 42 577

Countries citing papers authored by Julia E. Neumann

Since Specialization
Citations

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

Fields of papers citing papers by Julia E. Neumann

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Julia E. Neumann

This figure shows the co-authorship network connecting the top 25 collaborators of Julia E. Neumann. A scholar is included among the top collaborators of Julia E. Neumann 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 E. Neumann. Julia E. Neumann 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.
Dottermusch, Matthias, et al.. (2025). Spatial Proteomics Reveals Distinct Protein Patterns in Cortical Migration Disorders Caused by LIN28A Overexpression and WNT Activation. Molecular & Cellular Proteomics. 24(9). 101037–101037.
2.
Neumann, Julia E., et al.. (2025). HarmonizR: blocking and singular feature data adjustment improve runtime efficiency and data preservation. BMC Bioinformatics. 26(1). 47–47.
3.
Dottermusch, Matthias, et al.. (2024). Morphology‐based molecular classification of spinal cord ependymomas using deep neural networks. Brain Pathology. 34(5). e13239–e13239. 1 indexed citations
4.
Neumann, Julia E., et al.. (2024). Computational Methods for Data Integration and Imputation of Missing Values in Omics Datasets. PROTEOMICS. 25(1-2). e202400100–e202400100. 8 indexed citations
5.
6.
Neumann, Julia E., et al.. (2023). Robust classification using average correlations as features (ACF). BMC Bioinformatics. 24(1). 101–101. 1 indexed citations
7.
Schoof, Melanie, et al.. (2023). MYC overexpression and SMARCA4 loss cooperate to drive medulloblastoma formation in mice. Acta Neuropathologica Communications. 11(1). 174–174. 5 indexed citations
8.
Dottermusch, Matthias, Denise Obrecht, Stephan Frank, et al.. (2023). Integrated proteomics spotlight the proteasome as a therapeutic vulnerability in embryonal tumors with multilayered rosettes. Neuro-Oncology. 26(5). 935–949. 3 indexed citations
9.
Neumann, Julia E., et al.. (2022). The Second Side of the Coin—Resilience, Meaningfulness and Joyful Moments in Home Health Care Workers during the COVID-19 Pandemic. International Journal of Environmental Research and Public Health. 19(7). 3836–3836. 9 indexed citations
10.
Eichstädt, Sascha, et al.. (2022). THE QUALITY INFRASTRUCTURE IN THE DIGITAL AGE: BEYOND MACHINE-READABLE DOCUMENTS. 1–4. 3 indexed citations
11.
Neumann, Julia E., et al.. (2022). AN INTRODUCTION TO LINKED DATA AND THE SEMANTIC WEB. 1–4. 1 indexed citations
12.
Wurlitzer, Marcus, Matthias Dottermusch, Philipp Neumann, et al.. (2022). HarmonizR enables data harmonization across independent proteomic datasets with appropriate handling of missing values. Nature Communications. 13(1). 3523–3523. 33 indexed citations
13.
Schüller, Ulrich, Mario M. Dorostkar, Christian Mawrin, et al.. (2021). Mutations within FGFR1 are associated with superior outcome in a series of 83 diffuse midline gliomas with H3F3A K27M mutations. Acta Neuropathologica. 141(2). 323–325. 21 indexed citations
14.
Krisp, Christoph, Behnam Mohammadi, Matthias Dottermusch, et al.. (2021). Overexpression of Lin28A in neural progenitor cells in vivo does not lead to brain tumor formation but results in reduced spine density. Acta Neuropathologica Communications. 9(1). 185–185. 6 indexed citations
15.
Lauffer, Marlen C., Michael Bockmayr, Michael Spohn, et al.. (2019). TCF4 (E2-2) harbors tumor suppressive functions in SHH medulloblastoma. Acta Neuropathologica. 137(4). 657–673. 18 indexed citations
16.
Harms, Christoph, Julia E. Neumann, Günther Kundt, et al.. (2018). Parameters influencing hand grip strength measured with the manugraphy system. BMC Musculoskeletal Disorders. 19(1). 54–54. 18 indexed citations
17.
Mühldorfer‐Fodor, Marion, Christoph Harms, Julia E. Neumann, et al.. (2017). Load distribution of the hand during cylinder grip analyzed by Manugraphy. Journal of Hand Therapy. 30(4). 529–537. 21 indexed citations
18.
Neumann, Julia E., Fredrik J. Swartling, & Ulrich Schüller. (2017). Medulloblastoma: experimental models and reality. Acta Neuropathologica. 134(5). 679–689. 22 indexed citations
19.
Engel, Nils W., et al.. (2016). Canonical Wnt Signaling Drives Tumor-Like Lesions from Sox2-Positive Precursors of the Murine Olfactory Epithelium. PLoS ONE. 11(11). e0166690–e0166690. 5 indexed citations
20.
Gu, Song, Caiyi Zhang, Julia E. Neumann, et al.. (2016). Decreased demand for olfactory periglomerular cells impacts on neural precursor cell viability in the rostral migratory stream. Scientific Reports. 6(1). 32203–32203. 7 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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