Elmar Bucher

2.4k total citations
18 papers, 746 citations indexed

About

Elmar Bucher is a scholar working on Molecular Biology, Cancer Research and Biomedical Engineering. According to data from OpenAlex, Elmar Bucher has authored 18 papers receiving a total of 746 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Molecular Biology, 6 papers in Cancer Research and 3 papers in Biomedical Engineering. Recurrent topics in Elmar Bucher's work include Gene expression and cancer classification (6 papers), Cancer, Lipids, and Metabolism (3 papers) and Advanced Biosensing Techniques and Applications (3 papers). Elmar Bucher is often cited by papers focused on Gene expression and cancer classification (6 papers), Cancer, Lipids, and Metabolism (3 papers) and Advanced Biosensing Techniques and Applications (3 papers). Elmar Bucher collaborates with scholars based in United States, Finland and United Kingdom. Elmar Bucher's co-authors include Kristiina Iljin, Olli Kallioniemi, Laura M. Heiser, Paula Vainio, Henri Sara, Mari Björkman, Kalle Ojala, Sami Kilpinen, Saija Haapa-Paananen and Matthias Nees and has published in prestigious journals such as Journal of Clinical Investigation, PLoS ONE and Scientific Reports.

In The Last Decade

Elmar Bucher

15 papers receiving 736 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Elmar Bucher United States 10 514 205 183 108 79 18 746
Shiro Koizume Japan 18 487 0.9× 239 1.2× 170 0.9× 71 0.7× 126 1.6× 37 1.0k
Weiguo Wu United States 16 601 1.2× 152 0.7× 314 1.7× 80 0.7× 99 1.3× 33 978
Johanna M. Schafer United States 10 543 1.1× 196 1.0× 308 1.7× 88 0.8× 148 1.9× 18 1.0k
Chantal Pont Netherlands 11 408 0.8× 116 0.6× 132 0.7× 74 0.7× 49 0.6× 14 565
Jieyi Wang United States 15 443 0.9× 342 1.7× 291 1.6× 101 0.9× 69 0.9× 29 851
Vinothini Rajeeve United Kingdom 15 486 0.9× 113 0.6× 146 0.8× 149 1.4× 59 0.7× 37 823
Honor J. Hugo Australia 15 520 1.0× 238 1.2× 433 2.4× 91 0.8× 126 1.6× 29 927
Claudia Eichler-Jonsson Sweden 3 712 1.4× 125 0.6× 173 0.9× 89 0.8× 67 0.8× 5 947
Thomas Waerner Germany 10 636 1.2× 150 0.7× 306 1.7× 74 0.7× 59 0.7× 13 866

Countries citing papers authored by Elmar Bucher

Since Specialization
Citations

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

Fields of papers citing papers by Elmar Bucher

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Elmar Bucher

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

All Works

18 of 18 papers shown
1.
Wang, Yafei, John Metzcar, Elmar Bucher, et al.. (2025). Drug-loaded nanoparticles for cancer therapy: A high-throughput multicellular agent-based modeling study. Journal of Theoretical Biology. 616. 112266–112266.
2.
Eng, Jennifer, Elmar Bucher, Zhiwei Hu, et al.. (2025). Highly multiplexed imaging reveals prognostic immune and stromal spatial biomarkers in breast cancer. JCI Insight. 10(3). 3 indexed citations
3.
Eng, Jennifer, Elmar Bucher, Zhi Hu, et al.. (2022). A framework for multiplex imaging optimization and reproducible analysis. Communications Biology. 5(1). 438–438. 26 indexed citations
4.
Bucher, Elmar, et al.. (2022). Theoretical and experimental analysis of negative dielectrophoresis‐induced particle trajectories. Electrophoresis. 43(12). 1366–1377. 7 indexed citations
5.
Kersch, Cymon, Prakash Ambady, Elmar Bucher, et al.. (2020). Transcriptional signatures in histologic structures within glioblastoma tumors may predict personalized drug sensitivity and survival. Neuro-Oncology Advances. 2(1). vdaa093–vdaa093. 4 indexed citations
6.
Smith, Rebecca, Moqing Liu, Tiera Liby, et al.. (2020). Enzalutamide response in a panel of prostate cancer cell lines reveals a role for glucocorticoid receptor in enzalutamide resistant disease. Scientific Reports. 10(1). 21750–21750. 38 indexed citations
7.
Smith, Rebecca, Kaylyn L. Devlin, David Kilburn, et al.. (2019). Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer. Journal of Visualized Experiments. 9 indexed citations
8.
Bucher, Elmar, Rebecca Smith, Kaylyn L. Devlin, et al.. (2019). Annot: a Django-based sample, reagent, and experiment metadata tracking system. BMC Bioinformatics. 20(1). 542–542. 1 indexed citations
9.
Gross, Sean M., Mark Dane, Elmar Bucher, & Laura M. Heiser. (2019). Individual Cells Can Resolve Variations in Stimulus Intensity along the IGF-PI3K-AKT Signaling Axis. Cell Systems. 9(6). 580–588.e4. 23 indexed citations
10.
Devlin, Kaylyn L., David Kilburn, Sean M. Gross, et al.. (2019). Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer. Journal of Visualized Experiments.
11.
Watson, Spencer S., Mark Dane, Koei Chin, et al.. (2018). Microenvironment-Mediated Mechanisms of Resistance to HER2 Inhibitors Differ between HER2+ Breast Cancer Subtypes. Cell Systems. 6(3). 329–342.e6. 50 indexed citations
12.
Arjonen, Antti, Riina Kaukonen, Elina Mattila, et al.. (2014). Mutant p53–associated myosin-X upregulation promotes breast cancer invasion and metastasis. Journal of Clinical Investigation. 124(3). 1069–1082. 134 indexed citations
13.
Denkert, Carsten, Elmar Bucher, Mika Hilvo, et al.. (2012). Metabolomics of human breast cancer: new approaches for tumor typing and biomarker discovery. Genome Medicine. 4(4). 37–37. 79 indexed citations
14.
Brockmöller, Scarlet, Elmar Bucher, Berit Müller, et al.. (2011). Integration of Metabolomics and Expression of Glycerol-3-phosphate Acyltransferase (GPAM) in Breast Cancer—Link to Patient Survival, Hormone Receptor Status, and Metabolic Profiling. Journal of Proteome Research. 11(2). 850–860. 59 indexed citations
15.
Mpindi, John Patrick, Henri Sara, Saija Haapa-Paananen, et al.. (2011). GTI: A Novel Algorithm for Identifying Outlier Gene Expression Profiles from Integrated Microarray Datasets. PLoS ONE. 6(2). e17259–e17259. 24 indexed citations
16.
Mpindi, John Patrick, Henri Sara, Saija Haapa-Paananen, et al.. (2011). Correction: GTI: A Novel Algorithm for Identifying Outlier Gene Expression Profiles from Integrated Microarray Datasets. PLoS ONE. 6(4).
17.
Nord, Silje, Fredrik Johansen, Grethe I.G. Alnæs, et al.. (2008). Genome‐wide analysis identifies 16q deletion associated with survival, molecular subtypes, mRNA expression, and germline haplotypes in breast cancer patients. Genes Chromosomes and Cancer. 47(8). 680–696. 79 indexed citations
18.
Kilpinen, Sami, Reija Autio, Kalle Ojala, et al.. (2008). Systematic bioinformatic analysis of expression levels of 17,330 human genes across 9,783 samples from 175 types of healthy and pathological tissues. Genome biology. 9(9). R139–R139. 210 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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