Kenneth A. Feldmann

20.5k total citations · 8 hit papers
91 papers, 15.7k citations indexed

About

Kenneth A. Feldmann is a scholar working on Plant Science, Molecular Biology and Biochemistry. According to data from OpenAlex, Kenneth A. Feldmann has authored 91 papers receiving a total of 15.7k indexed citations (citations by other indexed papers that have themselves been cited), including 77 papers in Plant Science, 65 papers in Molecular Biology and 6 papers in Biochemistry. Recurrent topics in Kenneth A. Feldmann's work include Plant Molecular Biology Research (42 papers), Plant Reproductive Biology (33 papers) and Plant tissue culture and regeneration (19 papers). Kenneth A. Feldmann is often cited by papers focused on Plant Molecular Biology Research (42 papers), Plant Reproductive Biology (33 papers) and Plant tissue culture and regeneration (19 papers). Kenneth A. Feldmann collaborates with scholars based in United States, Japan and United Kingdom. Kenneth A. Feldmann's co-authors include M. David Marks, Joseph R. Ecker, Joseph J. Kieber, Gregg Roman, Madge Rothenberg, Suguru Takatsuto, Shozo Fujioka, Sunghwa Choe, Gary N. Drews and John L. Bowman and has published in prestigious journals such as Nature, Science and Cell.

In The Last Decade

Kenneth A. Feldmann

91 papers receiving 15.2k citations

Hit Papers

CTR1, a negative regulator of the ethylene response pathw... 1990 2026 2002 2014 1993 1990 1996 1996 1994 500 1000 1.5k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kenneth A. Feldmann United States 58 13.5k 10.9k 976 852 556 91 15.7k
Steven J. Rothstein Canada 69 10.6k 0.8× 7.3k 0.7× 763 0.8× 1.1k 1.3× 796 1.4× 164 14.0k
Dae‐Jin Yun South Korea 74 12.3k 0.9× 10.0k 0.9× 412 0.4× 457 0.5× 377 0.7× 265 16.4k
Bernd Mueller‐Roeber Germany 69 11.6k 0.9× 9.2k 0.8× 396 0.4× 542 0.6× 420 0.8× 180 14.5k
Bonnie Bartel United States 63 12.4k 0.9× 12.9k 1.2× 1.3k 1.4× 401 0.5× 272 0.5× 107 18.7k
Csaba Koncz Germany 71 13.6k 1.0× 12.0k 1.1× 418 0.4× 399 0.5× 303 0.5× 145 16.9k
Steven J. Clough United States 28 18.7k 1.4× 15.0k 1.4× 825 0.8× 319 0.4× 451 0.8× 60 22.1k
Bernd Weißhaar Germany 62 15.0k 1.1× 17.0k 1.6× 299 0.3× 936 1.1× 570 1.0× 165 21.7k
Jörg Kudla Germany 71 17.7k 1.3× 11.3k 1.0× 350 0.4× 415 0.5× 432 0.8× 137 21.0k
Shozo Fujioka Japan 73 16.8k 1.2× 12.2k 1.1× 344 0.4× 1.6k 1.8× 366 0.7× 215 19.2k
Richard M. Amasino United States 83 22.0k 1.6× 17.5k 1.6× 403 0.4× 1.5k 1.8× 1.1k 2.1× 165 24.5k

Countries citing papers authored by Kenneth A. Feldmann

Since Specialization
Citations

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

Fields of papers citing papers by Kenneth A. Feldmann

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kenneth A. Feldmann

This figure shows the co-authorship network connecting the top 25 collaborators of Kenneth A. Feldmann. A scholar is included among the top collaborators of Kenneth A. Feldmann 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 Kenneth A. Feldmann. Kenneth A. Feldmann 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
2.
Tatarinova, Tatiana V., Nickolai Alexandrov, John Bouck, & Kenneth A. Feldmann. (2010). GC3 biology in corn, rice, sorghum and other grasses. BMC Genomics. 11(1). 308–308. 111 indexed citations
3.
Wu, Chuanyin, Anthony Trieu, Shing F. Kwok, et al.. (2008). Brassinosteroids Regulate Grain Filling in Rice  . The Plant Cell. 20(8). 2130–2145. 316 indexed citations
4.
Alexandrov, Nickolai, Vyacheslav Brover, Maxim Troukhan, et al.. (2008). Insights into corn genes derived from large-scale cDNA sequencing. Plant Molecular Biology. 69(1-2). 179–194. 181 indexed citations
5.
Alexandrov, Nickolai, Maxim Troukhan, Vyacheslav Brover, et al.. (2006). Features of Arabidopsis Genes and Genome Discovered using Full-length cDNAs. Plant Molecular Biology. 60(1). 69–85. 123 indexed citations
6.
Haas, Brian J., Natalia Volfovsky, Christopher D. Town, et al.. (2002). Full-length messenger RNA sequences greatly improve genome annotation. Genome biology. 3(6). RESEARCH0029–RESEARCH0029. 146 indexed citations
7.
Feldmann, Kenneth A.. (2001). Cytochrome P450s as genes for crop improvement. Current Opinion in Plant Biology. 4(2). 162–167. 47 indexed citations
8.
Choe, Sunghwa, Shozo Fujioka, Takahiro Noguchi, et al.. (2001). Overexpression of DWARF4 in the brassinosteroid biosynthetic pathway results in increased vegetative growth and seed yield in Arabidopsis. The Plant Journal. 26(6). 573–582. 266 indexed citations
9.
Choe, Sunghwa, Atsushi Tanaka, Takahiro Noguchi, et al.. (2000). Lesions in the sterol Δ7 reductase gene of Arabidopsis cause dwarfism due to a block in brassinosteroid biosynthesis. The Plant Journal. 21(5). 431–443. 146 indexed citations
10.
Noguchi, Takahiro, Shozo Fujioka, Sunghwa Choe, et al.. (2000). Biosynthetic Pathways of Brassinolide in Arabidopsis. PLANT PHYSIOLOGY. 124(1). 201–210. 126 indexed citations
11.
Winkler, Rodney G. & Kenneth A. Feldmann. (1998). PCR-Based Identification of T-DNA Insertion Mutants. Humana Press eBooks. 82. 129–136. 13 indexed citations
12.
Dilkes, Brian P. & Kenneth A. Feldmann. (1998). Cloning Genes from T-DNA Tagged Mutants. Humana Press eBooks. 82. 339–351. 10 indexed citations
13.
McKinney, Elizabeth C., et al.. (1995). Sequence‐based identification of T‐DNA insertion mutations in Arabidopsis: actin mutants act2‐1 and act4‐1. The Plant Journal. 8(4). 613–622. 165 indexed citations
14.
Scholl, Randy, Kenneth A. Feldmann, & Andrew H. Paterson. (1994). 6 Quantitative Genetics. Cold Spring Harbor Monograph Archive. 27. 121–136. 4 indexed citations
15.
Lemieux, Bertrand, Maarten Koornneef, & Kenneth A. Feldmann. (1994). Epicuticular wax and eceriferum mutants.. Cold Spring Harbor Monograph Archive. 27. 1031–1047. 9 indexed citations
16.
Feldmann, Kenneth A., Russell L. Malmberg, & Caroline Dean. (1994). 7 Mutagenesis in Arabidopsis. Cold Spring Harbor Monograph Archive. 27. 137–172. 24 indexed citations
17.
Kowalski, S. P., et al.. (1994). QTL mapping of naturally-occurring variation in flowering time of Arabidopsis thaliana. Molecular and General Genetics MGG. 245(5). 548–555. 52 indexed citations
18.
Yadav, Narendra Singh, Anna Wierzbicki, Anthony J. Kinney, et al.. (1993). Cloning of Higher Plant [omega]-3 Fatty Acid Desaturases. PLANT PHYSIOLOGY. 103(2). 467–476. 224 indexed citations
19.
Kieber, Joseph J., Madge Rothenberg, Gregg Roman, Kenneth A. Feldmann, & Joseph R. Ecker. (1993). CTR1, a negative regulator of the ethylene response pathway in arabidopsis, encodes a member of the Raf family of protein kinases. Cell. 72(3). 427–441. 1520 indexed citations breakdown →
20.
Yanofsky, Martin F., Hong Mā, John L. Bowman, et al.. (1990). The protein encoded by the Arabidopsis homeotic gene agamous resembles transcription factors. Nature. 346(6279). 35–39. 1251 indexed citations breakdown →

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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