Craig A. Schenck

976 total citations
21 papers, 665 citations indexed

About

Craig A. Schenck is a scholar working on Molecular Biology, Plant Science and Biotechnology. According to data from OpenAlex, Craig A. Schenck has authored 21 papers receiving a total of 665 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Molecular Biology, 10 papers in Plant Science and 3 papers in Biotechnology. Recurrent topics in Craig A. Schenck's work include Plant biochemistry and biosynthesis (9 papers), Plant Gene Expression Analysis (8 papers) and Photosynthetic Processes and Mechanisms (6 papers). Craig A. Schenck is often cited by papers focused on Plant biochemistry and biosynthesis (9 papers), Plant Gene Expression Analysis (8 papers) and Photosynthetic Processes and Mechanisms (6 papers). Craig A. Schenck collaborates with scholars based in United States, Brazil and Netherlands. Craig A. Schenck's co-authors include Hiroshi Maéda, Robert L. Last, Bryan J. Leong, Peipei Wang, Bethany M. Moore, Pengxiang Fan, Shin‐Han Shiu, Federica Brandizzí, Pengfei Cao and Siyu Chen and has published in prestigious journals such as Proceedings of the National Academy of Sciences, PLANT PHYSIOLOGY and The FASEB Journal.

In The Last Decade

Craig A. Schenck

20 papers receiving 659 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Craig A. Schenck United States 12 450 330 68 58 42 21 665
Valentina Passeri Italy 14 335 0.7× 310 0.9× 52 0.8× 40 0.7× 30 0.7× 18 550
Xianbao Deng China 18 687 1.5× 494 1.5× 30 0.4× 81 1.4× 38 0.9× 40 923
Christopher M. Fraser United States 6 553 1.2× 487 1.5× 39 0.6× 40 0.7× 83 2.0× 8 858
M. Alizadeh Iran 10 375 0.8× 560 1.7× 75 1.1× 58 1.0× 45 1.1× 46 684
Brijmohan Singh Bhau India 15 309 0.7× 392 1.2× 87 1.3× 28 0.5× 29 0.7× 38 657
Chase F. Kempinski United States 11 349 0.8× 221 0.7× 58 0.9× 80 1.4× 48 1.1× 13 539
Bryan J. Leong United States 13 679 1.5× 287 0.9× 60 0.9× 85 1.5× 46 1.1× 20 848
Ashish R. Warghat India 13 294 0.7× 226 0.7× 40 0.6× 61 1.1× 40 1.0× 48 464
Dong Won Bae South Korea 17 397 0.9× 664 2.0× 62 0.9× 26 0.4× 56 1.3× 31 931
Houshang Alizadeh Iran 15 342 0.8× 477 1.4× 62 0.9× 33 0.6× 34 0.8× 31 716

Countries citing papers authored by Craig A. Schenck

Since Specialization
Citations

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

Fields of papers citing papers by Craig A. Schenck

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Craig A. Schenck

This figure shows the co-authorship network connecting the top 25 collaborators of Craig A. Schenck. A scholar is included among the top collaborators of Craig A. Schenck 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 Craig A. Schenck. Craig A. Schenck 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.
Busta, Lucas, et al.. (2025). A new spin on chemotaxonomy: Using non‐proteogenic amino acids as a test case. Applications in Plant Sciences. 13(4). e70006–e70006.
2.
Angelovici, Ruthie, et al.. (2024). Halophytes and heavy metals: A multi‐omics approach to understand the role of gene and genome duplication in the abiotic stress tolerance of Cakile maritima. American Journal of Botany. 111(8). e16310–e16310. 6 indexed citations
3.
Shakeel, Samina N., et al.. (2024). Molecular basis for thermogenesis and volatile production in the titan arum. PNAS Nexus. 3(11). pgae492–pgae492. 4 indexed citations
4.
Ortíz, Carlos E., Umesh K. Reddy, Petra Bleeker, et al.. (2024). Woolly mutation with the Get02 locus overcomes the polygenic nature of trichome-based pest resistance in tomato. PLANT PHYSIOLOGY. 195(2). 911–923. 7 indexed citations
5.
Peres, Lázaro Eustáquio Pereira, et al.. (2024). Sticky business: the intricacies of acylsugar biosynthesis in the Solanaceae. Phytochemistry Reviews. 24(4). 2485–2500. 1 indexed citations
6.
Angelovici, Ruthie, et al.. (2024). Mechanism of action of the toxic proline mimic azetidine 2‐carboxylic acid in plants. The Plant Journal. 120(6). 2904–2918. 2 indexed citations
7.
Schenck, Craig A., et al.. (2022). Natural variation meets synthetic biology: Promiscuous trichome-expressed acyltransferases from Nicotiana. PLANT PHYSIOLOGY. 190(1). 146–164. 8 indexed citations
8.
Schenck, Craig A. & Lucas Busta. (2021). Using interdisciplinary, phylogeny-guided approaches to understand the evolution of plant metabolism. Plant Molecular Biology. 109(4-5). 355–367. 13 indexed citations
9.
Fan, Pengxiang, Peipei Wang, Yann‐Ru Lou, et al.. (2020). Evolution of a plant gene cluster in Solanaceae and emergence of metabolic diversity. eLife. 9. 58 indexed citations
10.
Moore, Bethany M., Peipei Wang, Pengxiang Fan, et al.. (2020). Within- and cross-species predictions of plant specialized metabolism genes using transfer learning. PubMed. 2(1). diaa005–diaa005. 18 indexed citations
11.
Schenck, Craig A., Dhileepkumar Jayaraman, Kevin Garcia, et al.. (2020). Role of cytosolic, tyrosine‐insensitive prephenate dehydrogenase in Medicago truncatula. Plant Direct. 4(5). e00218–e00218. 13 indexed citations
12.
Moore, Bethany M., Peipei Wang, Pengxiang Fan, et al.. (2019). Robust predictions of specialized metabolism genes through machine learning. Proceedings of the National Academy of Sciences. 116(6). 2344–2353. 82 indexed citations
13.
Cao, Pengfei, Sang‐Jin Kim, Anqi Xing, et al.. (2019). Homeostasis of branched-chain amino acids is critical for the activity of TOR signaling in Arabidopsis. eLife. 8. 82 indexed citations
14.
Schenck, Craig A. & Robert L. Last. (2019). Location, location! cellular relocalization primes specialized metabolic diversification. FEBS Journal. 287(7). 1359–1368. 26 indexed citations
15.
Schenck, Craig A. & Hiroshi Maéda. (2018). Tyrosine biosynthesis, metabolism, and catabolism in plants. Phytochemistry. 149. 82–102. 200 indexed citations
16.
Schenck, Craig A., et al.. (2017). Molecular basis of the evolution of alternative tyrosine biosynthetic routes in plants. Nature Chemical Biology. 13(9). 1029–1035. 50 indexed citations
17.
Schenck, Craig A., et al.. (2017). Conserved Molecular Mechanism of TyrA Dehydrogenase Substrate Specificity Underlying Alternative Tyrosine Biosynthetic Pathways in Plants and Microbes. Frontiers in Molecular Biosciences. 4. 73–73. 13 indexed citations
18.
Schenck, Craig A., Siyu Chen, Daniel L. Siehl, & Hiroshi Maéda. (2014). Non-plastidic, tyrosine-insensitive prephenate dehydrogenases from legumes. Nature Chemical Biology. 11(1). 52–57. 57 indexed citations
19.
Schenck, Craig A., et al.. (2013). A proteomics approach identifies novel proteins involved in gravitropic signal transduction. American Journal of Botany. 100(1). 194–202. 20 indexed citations
20.
Luesse, Darron R., et al.. (2010). GPS4 IS ALLELIC TO ARL2: IMPLICATIONS FOR GRAVITROPIC SIGNAL TRANSDUCTION. Gravitational and Space Research. 23(2). 4 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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