Author

C. A. Schenck

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

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Plant Biology· 4

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Research Focus:gene regulatory network inference· 4 Research Focus:differential expression· 3 Assay:DNA methylation· 3 Organism:plant (other)· 3 Assay:whole-genome sequencing· 3 Organism:Arabidopsis· 2 Research Focus:cell type annotation· 2 Research Focus:chromatin & epigenetic regulation· 1

Frequent Co-Authors

P. Fan· 3 shared P. Wang· 3 shared S.-H. Shiu· 3 shared B. Leong· 2 shared B. M. Moore· 2 shared R. Last· 2 shared A. Lee· 1 shared B. J. Leong· 1 shared

Activity by Year

2019-2020 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 35 mutual co-author links

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3 shared papersPlant Biology

P. Fan

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4 papers

Recent·Most cited

Evolution of a plant gene cluster in Solanaceae and emergence of metabolic diversity

P. Fan, P. Wang, Y.-R. Lou, B. J. Leong, B. J. Moore, C. A. Schenck, R. Combs, P. Cao, F. Brandizzi, S.-H. Shiu, R. L. Last

The paper studies the evolution of a gene cluster in Solanaceae plants and its role in producing medium chain acylsugars through trichome-specific enzymes.

bioRxiv · Mar 4, 2020Plant Biology

Within and cross species predictions of plant specialized metabolism genes using transfer learning

B. M. Moore, P. Wang, P. Fan, A. Lee, B. Leong, Y.-L. Lou, C. A. Schenck, K. Sugimoto, R. Last, M. D. Lehti-Shiu, C. Barry, S.-H. Shiu

The paper uses transfer learning to predict specialized metabolism genes in tomato by leveraging annotations from Arabidopsis thaliana, improving prediction accuracy.

bioRxiv · Jan 13, 2020Plant Biology

Role of Cytosolic, Tyrosine-Insensitive Prephenate Dehydrogenase in Medicago truncatula

C. A. Schenck, J. Westphal, D. Jayaraman, K. Garcia, J. Wen, K. S. Mysore, J.-M. Anee, L. W. Sumner, H. A. Maeda

This study investigates the role of PDH in Medicago truncatula by analyzing mutants, finding that MtPDH1 contributes to Tyr biosynthesis under stimulated shikimate pathway conditions.

bioRxiv · Sep 13, 2019Plant Biology

Robust predictions of specialized metabolism genes through machine learning

B. M. Moore, P. Wang, P. Fan, B. Leong, C. A. Schenck, J. P. Lloyd, M. Lehti-Shiu, R. Last, E. Pichersky, S.-H. Shiu

The paper develops a machine learning model to predict specialized metabolism genes in Arabidopsis thaliana by integrating multiple genomic features, achieving high accuracy in identification.

Proceedings of the National Academy of Sciences · Jan 23, 2019Peer-reviewedPlant Biology