K. Feldmann

30 papers receiving 670 citations

Peers

K. Feldmann
Comparison fields: 5 of 72
  • Microbiology 46
  • Infectious Diseases 455
  • Epidemiology 415
  • Small Animals 57
  • Molecular Medicine 35
Replace Krishnamoorthy Gopinath with:
Krishnamoorthy Gopinath India
Catherine Pierre-Audigier France
Yuko Kazumi Japan
Solange Alves Vinhas Brazil
Akio Aono Japan
M. T. Simonetti Italy
Е. Е. Ларионова Russia
Julie V. Philley United States
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N Martín-Casabona Spain
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Citations per field
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Krishnamoorthy Gopinath · 1×
Citations per year

Countries citing papers authored by K. Feldmann

Since Specialization
Citations

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

Fields of papers citing papers by K. Feldmann

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside K. Feldmann, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with K. Feldmann Line = papers co-authored together K. Feldmann links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 31 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2008105
2 199688
3 199876
4 199670
5 197765
6
Usefulness of Mycobacterium tuberculosis genomic mutations in the genes katG and inhA for the prediction of isoniazid resistance.
199756
7 199644
8 199725
9 199825
10 200124
11 199622
12 199622
13
Deconfounding microarray analysis - independent measurements of cell type proportions used in a regression model to resolve tissue heterogeneity bias.
200619
14
Molecular Detection of Drug-Resistant Tuberculosis by Line Probe Assay. Laboratory Manual for Resource-Limited Settings.
201213
15 200412
16 198011
17 199610
18 19999
19 20006
20 19816

About K. Feldmann

K. Feldmann is a scholar working on Infectious Diseases, Epidemiology, Molecular Biology, Surgery and Radiology, Nuclear Medicine and Imaging, having authored 31 papers that have together received 732 indexed citations. Recurring topics across this work include Tuberculosis Research and Epidemiology (15 papers), Mycobacterium research and diagnosis (11 papers), Diagnosis and treatment of tuberculosis (4 papers), Peptidase Inhibition and Analysis (3 papers), Advanced MRI Techniques and Applications (3 papers), Genomics and Phylogenetic Studies (3 papers), Medical Imaging Techniques and Applications (2 papers) and Lung Cancer Research Studies (2 papers). The work is most often cited by research in Microbiology (46 citations), Infectious Diseases (455 citations), Epidemiology (415 citations), Small Animals (57 citations) and Molecular Medicine (35 citations). K. Feldmann has collaborated with scholars based in Germany, United States and Vietnam. Frequent co-authors include William E. Hull, Heinz Rinder, T. Löscher, Burkhard Springer, Paul A. Kirschner, Gisela Bretzel, Jens Rosenau, Erik C. Böttger, Sabine Rüsch–Gerdes and Sabine Hofmann-Thiel. Their work appears in journals such as Journal of Clinical Microbiology, Journal of Clinical Oncology, European Respiratory Journal, American Journal of Tropical Medicine and Hygiene and Proceedings of the National Academy of Sciences.

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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