Daniel E. Kahne
Impact in
- Molecular Medicine top 0.05%
- Antibiotic Resistance in Bacteria
- Endocrinology top 0.1%
Papers in
- Genetics 97
- Bacterial Genetics and Biotechnology 96
-
- Antibiotic Resistance in Bacteria 36
- Co-authors
- Thomas J. Silhavy (23 shared papers)Natividad Ruiz (31 shared papers)Suzanne Walker (61 shared papers)Christine L. Hagan (8 shared papers)Seokhee Kim (4 shared papers)Juliana C. Malinverni (4 shared papers)Shu‐Sin Chng (7 shared papers)Tao Wu (5 shared papers)
- Journals
- Journal of the American Chemical Society (71 papers)Proceedings of the National Academy of Sciences (29 papers)Biochemistry (14 papers)Science (9 papers)Nature (7 papers)
- Partner nations
- United StatesJapanItaly
In The Last Decade
Daniel E. Kahne
206 papers receiving 17.1k citations
Daniel E. Kahne's Hit Papers
Peers
Comparison fields: 5 of 145
- Molecular Medicine 3.2k
- Endocrinology 1.7k
- Genetics 6.4k
- Molecular Biology 11.4k
- Microbiology 1.0k
Countries citing papers authored by Daniel E. Kahne
This map shows the geographic impact of Daniel E. Kahne'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 Daniel E. Kahne with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel E. Kahne more than expected).
Fields of papers citing papers by Daniel E. Kahne
This network shows the impact of papers produced by Daniel E. Kahne. 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 Daniel E. Kahne. The network helps show where Daniel E. Kahne may publish in the future.
Co-authors
The 25 scholars most cited alongside Daniel E. Kahne, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 207 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2005 | 638 | |
| 2 | 1989 | 433 | |
| 3 | 2016 | 380 | |
| 4 | 2005 | 377 | |
| 5 | 2006 | 317 | |
| 6 | 2007 | 316 | |
| 7 | 2016 | 301 | |
| 8 | 2013 | 295 | |
| 9 | 1999 | 294 | |
| 10 | 2011 | 271 | |
| 11 | 2006 | 270 | |
| 12 | 2005 | 262 | |
| 13 | 2010 | 262 | |
| 14 | 2007 | 256 | |
| 15 | 2014 | 246 | |
| 16 | 2017 | 244 | |
| 17 | 1996 | 231 | |
| 18 | 2019 | 230 | |
| 19 | 2010 | 228 | |
| 20 | 2008 | 226 |
About Daniel E. Kahne
Daniel E. Kahne is a scholar working on Genetics, Molecular Medicine, Molecular Biology, Organic Chemistry and Pharmacology, having authored 207 papers that have together received 17.9k indexed citations. Recurring topics across this work include Bacterial Genetics and Biotechnology (96 papers), RNA and protein synthesis mechanisms (59 papers), Carbohydrate Chemistry and Synthesis (48 papers), Glycosylation and Glycoproteins Research (44 papers), Antibiotic Resistance in Bacteria (36 papers), Microbial Natural Products and Biosynthesis (24 papers), Bacteriophages and microbial interactions (24 papers) and Genomics and Phylogenetic Studies (22 papers). The work is most often cited by research in Molecular Medicine (3.2k citations), Endocrinology (1.7k citations), Genetics (6.4k citations), Molecular Biology (11.4k citations) and Microbiology (1.0k citations). Daniel E. Kahne has collaborated with scholars based in United States, Japan and Italy. Frequent co-authors include Thomas J. Silhavy, Natividad Ruiz, Suzanne Walker, Christine L. Hagan, Seokhee Kim, Juliana C. Malinverni, Shu‐Sin Chng, Tao Wu, Luisa S. Gronenberg and Lin Yan. Their work appears in journals such as Journal of the American Chemical Society, Proceedings of the National Academy of Sciences, Biochemistry, Science and Nature.
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.