Keywords: paralog processing, machine learning, bread wheat, drought tolerance

Latest 34 Papers

Secretory carrier membrane proteins assist with aquaporin trafficking in Arabidopsis.

Authors: Jiang, Q., Vandorpe, M., fox, a. R., Vermeersch, M., Mylle, E., Cuadrado, A. F., Kraus, J., Liu, H., Eeckhout, D., Navarre, C., Courtoy, A., Jacobs, T. B., Dragwidge, J. M., De Smet, I., Pleskot, R., Chaumont, F., Van Damme, D.

Date: 2025-07-06 · Version: 1

DOI: 10.1101/2025.07.03.662988

Model Organism: Arabidopsis thaliana

SCAMP proteins Arabidopsis thaliana aquaporins (PIPs)drought tolerance protein trafficking

Integrating Load-Cell Lysimetry and Machine Learning for Prediction of Daily Plant Transpiration

Authors: Friedman, S., Averbuch, N., Nevo, T., Moshelion, M.

Date: 2025-06-30 · Version: 2

DOI: 10.1101/2024.11.24.625038

Model Organism: Multi-species

daily transpiration machine learning gravimetric load cells Random Forest XGBoost

Tomato leaf transcriptomic changes promoted by long-term water scarcity stress can be largely prevented by a fungal-based biostimulant

Authors: Lopez-Serrano, L., Ferez-Gomez, A., Romero-Aranda, R., Jaime Fernandez, E., Leal Lopez, J., Fernandez Baroja, E., Almagro, G., Dolezal, K., Novak, O., Diaz, L., Bautista, R., Leon Morcillo, R. J., Pozueta Romero, J.

Date: 2025-06-28 · Version: 1

DOI: 10.1101/2025.06.26.661737

Model Organism: Solanum lycopersicum

water deficit Trichoderma harzianum culture filtrate tomato drought tolerance transcriptome

The biofuel producing legume tree, Pongamia pinnata, offers strong potential for carbon sequestration

Authors: Chen, A., Scott, P., Gresshoff, P. M., Ferguson, B. J.

Date: 2025-06-08 · Version: 1

DOI: 10.1101/2025.06.04.657758

Model Organism: Pongamia pinnata

Pongamia pinnata biofuel feedstock carbon sequestration growth variability drought tolerance

Endophytes induce systemic spatial reprogramming of metabolism in poplar roots under drought

Authors: Aufrecht, J. A., Velickovic, D., Tournay, R., Couvillion, S. P., Balasubramanian, V. K., Winkler, T., Herrera, D., Stanley, R., Doty, S., Ahkami, A. H.

Date: 2025-06-06 · Version: 1

DOI: 10.1101/2025.06.02.657501

Model Organism: General

beneficial endophytes drought stress root metabolome chemical imaging machine learning

Multiplexed profiling of transcriptional regulators in plant cells

Authors: Alamos, S., Waldburger, L., Dee, A., Owens, L., Rattan, R., Ong, S., Shih, P.

Date: 2025-05-14 · Version: 1

DOI: 10.1101/2025.05.12.653590

Model Organism: General

transcriptional regulation high-throughput plant assay protein‑coding library machine learning synthetic protein engineering

Phylogenetic Analysis and Machine Learning Identify Signatures of Selection and Predict Deleterious Mutations in Common Bean

Authors: Cordoba-Novoa, H. A., Buckler, E. S., Romay, C., Berthel, A., Johnson, L., Balasubramanian, P., Hoyos Villegas, V.

Date: 2025-05-06 · Version: 1

DOI: 10.1101/2025.05.05.652309

Model Organism: Phaseolus vulgaris

deleterious mutations genetic load common bean MAGIC population machine learning

S2-PepAnalyst: A Web Tool for Predicting Plant Small Signalling Peptides

Authors: Vomo Donfack, K. L., Abaach, M., Luna, A. M., Ginot, G., Doblas, V. G., Morilla, I.

Date: 2025-05-06 · Version: 2

DOI: 10.1101/2024.08.02.606319

Model Organism: General

small signalling peptides machine learning protein language models geometric-topological analysis reinforcement learning

Loss-of-function of the drought-induced genes GASA3 and AFP1 confers enhanced drought tolerance in Arabidopsis thaliana

Authors: Bhattacharyya, S., Turysbek, B., Lorenz, S. D., Rosales, D. C., Shoaib, Y., Gutbrod, K., Doermann, P., Chigri, F., Vothknecht, U. C.

Date: 2025-04-06 · Version: 1

DOI: 10.1101/2025.04.03.647048

Model Organism: Arabidopsis thaliana

drought tolerance GASA3 AFP1 abscisic acid (ABA)stomatal aperture

High Density Phenotypic Map of Natural Variation for Intermediate Phenotypes Associated with Stalk Lodging Resistance in Maize

Authors: Kunduru, B., Bokros, N. T., Tabaracci, K., Kumar, R., Brar, M. S., Stubbs, C. J., Oduntan, Y., DeKold, J., Bishop, R. H., Woomer, J., Verges, V. L., McDonald, A., McMahan, C. S., DeBolt, S., Robertson, D. J., Sekhon, R.

Date: 2025-04-06 · Version: 1

DOI: 10.1101/2025.04.03.647088

Model Organism: Zea mays

stalk lodging resistance maize phenomics genetic architecture intermediate phenotypes machine learning

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