Author

T. Z. Jubery

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:clustering· 3 Assay:imaging· 3 Organism:plant (other)· 3 Research Focus:cell type annotation· 2 Approach:deep learning· 2 Research Focus:differential expression· 2 Approach:contrastive learning· 1 Research Focus:dimensionality reduction· 1

Frequent Co-Authors

B. Ganapathysubramanian· 3 shared A. K. Singh· 1 shared A. Singh· 1 shared A. V. Nishimwe· 1 shared C. Carley· 1 shared D. Dong· 1 shared G. Sun· 1 shared J. C. Schnable· 1 shared

Activity by Year

2020-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 19 mutual co-author links

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3 shared papersPlant Biology

B. Ganapathysubramanian

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Author profile →

3 papers

Recent·Most cited

Data driven discovery and quantification of hyperspectral leaf reflectance phenotypes across a maize diversity panel

M. C. Tross, M. W. Grzybowski, T. Z. Jubery, R. J. Grove, A. V. Nishimwe, J. V. Torres-Rodriguez, G. Sun, B. Ganapathysubramanian, J. C. Schnable

This study uses data-driven methods to analyze hyperspectral leaf reflectance data from maize plants, identifying genetic markers correlated with reflectance patterns and chlorophyll content.

bioRxiv · Dec 15, 2023Plant Biology

Self-Supervised Maize Kernel Classification and Segmentation for Embryo Identification

D. Dong, K. Nagasubramanian, R. Wang, U. K. Frei, T. Z. Jubery, T. Lübberstedt, B. Ganapathysubramanian

This paper applies self-supervised learning to maize kernel classification and segmentation, showing improved performance and annotation efficiency compared to supervised methods.

Frontiers in Plant Science · Apr 14, 2023Peer-reviewedPlant Biology

Using Machine Learning To Develop A Fully Automated Soybean Nodule Acquisition Pipeline (SNAP)

T. Z. Jubery, C. Carley, S. Sarkar, A. Singh, B. Ganapathysubramanian, A. K. Singh

The paper introduces SNAP, a deep learning pipeline for automated soybean nodule quantification with 99% accuracy, enabling efficient analysis of nodule traits and genetic-environmental interactions.

bioRxiv · Oct 12, 2020Plant Biology