K. Feldmann

913 citations
24 papers · 762 · h-index 13

Impact in

Papers in

K. Feldmann

24 papers receiving 685 citations

Peers

K. Feldmann
Comparison fields: 5 of 51
  • Microbiology 40
  • Infectious Diseases 428
  • Clinical Biochemistry 47
  • Epidemiology 130
  • Endocrinology 13
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Benito B. Diena Canada
Elisabeth Hodille France
Werner B. Schaefer United States
R Guinet France
Yuriko Ozeki Japan
Juan de Dios Caballero Spain
Jelena Catania United States
Maurice L. Cohn United States
Nuran Yuluğ Türkiye
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Citations per field
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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 24 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2008111
2 199688
3 199882
4 199672
5 197768
6
Usefulness of Mycobacterium tuberculosis genomic mutations in the genes katG and inhA for the prediction of isoniazid resistance.
199759
7 199650
8 199827
9 200126
10 199725
11 199624
12 199622
13
Deconfounding microarray analysis - independent measurements of cell type proportions used in a regression model to resolve tissue heterogeneity bias.
200620
14 198013
15 200413
16
Molecular Detection of Drug-Resistant Tuberculosis by Line Probe Assay. Laboratory Manual for Resource-Limited Settings.
201213
17 199610
18 199910
19 200010
20 20019

About K. Feldmann

K. Feldmann is a scholar working on Infectious Diseases, Clinical Biochemistry, Epidemiology, Spectroscopy and Urology, having authored 24 papers that have together received 762 indexed citations. Recurring topics across this work include Tuberculosis Research and Epidemiology (13 papers), Mycobacterium research and diagnosis (5 papers), Bacterial Identification and Susceptibility Testing (3 papers), Sepsis Diagnosis and Treatment (2 papers), Advanced NMR Techniques and Applications (2 papers), Dermatological diseases and infestations (1 paper), Biochemical and Molecular Research (1 paper) and Cardiomyopathy and Myosin Studies (1 paper). The work is most often cited by research in Microbiology (40 citations), Infectious Diseases (428 citations), Clinical Biochemistry (47 citations), Epidemiology (130 citations) and Endocrinology (13 citations). K. Feldmann has collaborated with scholars based in Germany, Spain and Switzerland. Frequent co-authors include William E. Hull, Heinz Rinder, T. Löscher, Gisela Bretzel, Sabine Rüsch–Gerdes, Burkhard Springer, Erik C. Böttger, Jens Rosenau, Jakko van Ingen and Dick van Soolingen. Their work appears in journals such as Journal of Clinical Microbiology, European Respiratory Journal, Journal of Molecular Medicine, Molecular Diagnosis and American Journal of Tropical Medicine and Hygiene.

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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