Michael Kaufmann

1.5k total citations
40 papers, 1.3k citations indexed

About

Michael Kaufmann is a scholar working on Molecular Biology, Materials Chemistry and Biomedical Engineering. According to data from OpenAlex, Michael Kaufmann has authored 40 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Molecular Biology, 8 papers in Materials Chemistry and 5 papers in Biomedical Engineering. Recurrent topics in Michael Kaufmann's work include Genomics and Phylogenetic Studies (6 papers), Metal-Organic Frameworks: Synthesis and Applications (5 papers) and Enzyme Structure and Function (4 papers). Michael Kaufmann is often cited by papers focused on Genomics and Phylogenetic Studies (6 papers), Metal-Organic Frameworks: Synthesis and Applications (5 papers) and Enzyme Structure and Function (4 papers). Michael Kaufmann collaborates with scholars based in Germany, United States and China. Michael Kaufmann's co-authors include Wenbin Lin, Zhe Li, Kaiyuan Ni, Taokun Luo, Guangxu Lan, Xuanyu Feng, Justin S. Chen, Yang Song, Cheng Wang and August Culbert and has published in prestigious journals such as Journal of the American Chemical Society, Journal of Biological Chemistry and Bioinformatics.

In The Last Decade

Michael Kaufmann

40 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Michael Kaufmann Germany 13 444 361 321 307 139 40 1.3k
Mi Young Cho South Korea 29 449 1.0× 692 1.9× 448 1.4× 134 0.4× 18 0.1× 93 2.1k
Viet Quoc Nguyen United States 16 251 0.6× 273 0.8× 137 0.4× 162 0.5× 95 0.7× 59 1.3k
Dorit Michaeli Israel 19 194 0.4× 1.5k 4.1× 164 0.5× 85 0.3× 531 3.8× 29 2.3k
Tim Rasmussen United Kingdom 24 139 0.3× 1.1k 3.0× 91 0.3× 146 0.5× 158 1.1× 48 1.7k
Lilian Jacquamet France 25 552 1.2× 680 1.9× 113 0.4× 255 0.8× 176 1.3× 35 1.7k
Paola Laurino Japan 19 321 0.7× 1.0k 2.8× 357 1.1× 61 0.2× 32 0.2× 44 1.6k
Melissa S. T. Koay Netherlands 22 246 0.6× 789 2.2× 127 0.4× 49 0.2× 55 0.4× 30 1.5k
Christopher Bradburne United States 18 402 0.9× 786 2.2× 243 0.8× 29 0.1× 76 0.5× 33 1.3k
Brian C. Tripp United States 18 164 0.4× 978 2.7× 109 0.3× 38 0.1× 88 0.6× 26 1.4k

Countries citing papers authored by Michael Kaufmann

Since Specialization
Citations

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

Fields of papers citing papers by Michael Kaufmann

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michael Kaufmann

This figure shows the co-authorship network connecting the top 25 collaborators of Michael Kaufmann. A scholar is included among the top collaborators of Michael Kaufmann 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 Michael Kaufmann. Michael Kaufmann 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.
Aerts, Niels, Richard Hickman, Anja J.H. Van Dijken, et al.. (2024). Architecture and dynamics of the abscisic acid gene regulatory network. The Plant Journal. 119(5). 2538–2563. 7 indexed citations
2.
Kaufmann, Michael, Karin Schork, Michael Turewicz, et al.. (2022). MicroRNAs from urinary exosomes as alternative biomarkers in the differentiation of benign and malignant prostate diseases. PubMed. 11. 5–13. 11 indexed citations
3.
Kaufmann, Michael, et al.. (2021). Small RNAs as biomarkers to differentiate benign and malign prostate diseases: An alternative for transrectal punch biopsy of the prostate?. PLoS ONE. 16(3). e0247930–e0247930. 15 indexed citations
4.
Song, Yang, Zhe Li, Pengfei Ji, et al.. (2019). Metal–Organic Framework Nodes Support Single-Site Nickel(II) Hydride Catalysts for the Hydrogenolysis of Aryl Ethers. ACS Catalysis. 9(2). 1578–1583. 71 indexed citations
5.
Steinbach, Daniel, et al.. (2019). High Detection Rate for Non–Muscle-Invasive Bladder Cancer Using an Approved DNA Methylation Signature Test. Clinical Genitourinary Cancer. 18(3). 210–221. 9 indexed citations
6.
Liu, Jing, Philip Boehme, Wenli Zhang, et al.. (2018). Human adenovirus type 17 from species D transduces endothelial cells and human CD46 is involved in cell entry. Scientific Reports. 8(1). 13442–13442. 11 indexed citations
7.
Kaufmann, Michael, et al.. (2012). ANCAC: amino acid, nucleotide, and codon analysis of COGs – a tool for sequence bias analysis in microbial orthologs. BMC Bioinformatics. 13(1). 223–223. 5 indexed citations
8.
Kaltschmidt, Barbara, et al.. (2009). On the cytotoxicity of HCR-NTPase in the neuroblastoma cell line SH-SY5Y. BMC Research Notes. 2(1). 102–102. 4 indexed citations
9.
Kaufmann, Michael, et al.. (2008). Extension of the COG and arCOG databases by amino acid and nucleotide sequences. BMC Bioinformatics. 9(1). 479–479. 7 indexed citations
10.
Kaufmann, Michael, et al.. (2004). PCOGR: Phylogenetic COG ranking as an online tool to judge the specificity of COGs with respect to freely definable groups of organisms. BMC Bioinformatics. 5(1). 150–150. 298 indexed citations
11.
Kaufmann, Michael, et al.. (2003). EPPS: mining the COG database by an extended phylogeneticpatterns search. Bioinformatics. 19(6). 784–785. 14 indexed citations
12.
Kaufmann, Michael, et al.. (2003). Thermophile-specific proteins: the gene product of aq_1292 from Aquifex aeolicus is an NTPase. BMC Biochemistry. 4(1). 12–12. 8 indexed citations
13.
14.
Kaufmann, Michael. (1997). Creature Comforts: Animal-Assisted Activities in Education and Therapy. 1(2). 27–31. 7 indexed citations
15.
Kaufmann, Michael. (1997). Unstable proteins: how to subject them to chromatographic separations for purification procedures. Journal of Chromatography B Biomedical Sciences and Applications. 699(1-2). 347–369. 62 indexed citations
16.
Schwarz, Thomas L., et al.. (1997). Multifunctional Tryptophan-synthesizing Enzyme. Journal of Biological Chemistry. 272(16). 10616–10623. 5 indexed citations
17.
Schwarz, Thomas L., et al.. (1994). The use of a hollow fiber membrane module in sample conditioning prior to electrophoresis. Electrophoresis. 15(1). 1118–1119. 5 indexed citations
18.
Kühn, Walter, et al.. (1987). Zur Karzinomentstehung (Vulvakarzinom) beim Netherton-Syndrom (Ichthyosis, Haaranomalien, atopische Diathese). Geburtshilfe und Frauenheilkunde. 47(10). 742–744. 11 indexed citations
19.
Kaufmann, Michael, et al.. (1978). [Short-term incubation in vitro with precursors of nucleic acids on human primary tumors and metastases of carcinoma of the breast (author's transl)].. Munich Personal RePEc Archive (Ludwig Maximilian University of Munich). 154(4). 277–81. 4 indexed citations
20.
Volm, M, Michael Kaufmann, J Mattern, & K. Wayss. (1975). [Sensitivity tests of malignant tumours against cytostatic agents in vitro and in vivo/studies on the mouse sarcoma 180 (author's transl)].. Munich Personal RePEc Archive (Ludwig Maximilian University of Munich). 25(7). 1042–8. 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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