Author

R. Last

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

Biology Disciplines

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Plant Biology· 3

Signature Assays, Organisms & Methods

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Research Focus:differential expression· 3 Research Focus:gene regulatory network inference· 3 Organism:Arabidopsis· 2 Research Focus:cell type annotation· 2 Assay:DNA methylation· 2 Organism:plant (other)· 2 Assay:whole-genome sequencing· 2 Assay:bulk RNA-seq· 1

Frequent Co-Authors

B. Leong· 2 shared B. M. Moore· 2 shared C. A. Schenck· 2 shared P. Fan· 2 shared P. Wang· 2 shared S.-H. Shiu· 2 shared A. Lee· 1 shared A. T. Merrill· 1 shared

Activity by Year

2019-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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2 shared papersPlant Biology

B. Leong

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Author profile →

3 papers

Recent·Most cited

Comparative transcriptomics and metabolomics reveal specialized metabolite drought stress responses in switchgrass (Panicum virgatum L.)

K. J. Tiedge, X. Li, A. T. Merrill, D. L. Davisson, Y. Chen, P. Yu, D. J. Tantillo, R. Last, P. Zerbe

This study compares transcriptomic and metabolomic responses to drought in two switchgrass genotypes, identifying specialized diterpenoids as key to drought tolerance.

bioRxiv · Apr 20, 2022Plant 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

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