Keywords: paralog processing, machine learning, transcriptomic analysis, drought tolerance

Latest 38 Papers

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

Developing the Grapevine Hydric Stress Atlas: A Meta-Analysis Resource for Exploring transcriptome Responses to Drought

Authors: VALENZUELA, A. V., Navarro-Paya, D., Santiago, A., Sonego, P., Gainza-Cortes, F., Malnoy, M., Matus, J. T.

Date: 2025-04-01 · Version: 1

DOI: 10.1101/2025.03.31.646369

Model Organism: Vitis vinifera

drought tolerance grapevine (Vitis vinifera)transcriptomic meta-analysis gene co‑expression network molecular breeding

Priming of retrograde signaling in wheat across multiple natural environments reveal how responses to dynamic stimuli can be integrated to alter yield, yield stability and water productivity

Authors: Bowerman, A. F., Moore, M., Yadav, A., Zhang, J., Mortimer, M. D., Plskova, Z., Tee, E. E., Au, E. K., Collinge, D. P., Estavillo, G. M., Howitt, C. A., Chan, K. X., Rebetzke, G. J., Pogson, B. J.

Date: 2025-03-13 · Version: 1

DOI: 10.1101/2025.03.10.642515

Model Organism: Triticum aestivum

retrograde signaling SAL gene deletions wheat (Triticum aestivum)drought tolerance field trial validation

Sunflower yield modeling with XAI: Historical weather impacts and forecasting

Authors: Majumder, S., Mason, C.

Date: 2025-02-28 · Version: 1

DOI: 10.1101/2025.02.27.640573

Model Organism: Helianthus annuus

explainable AI sunflower yield climate variability machine learning Shared Socioeconomic Pathways

Robustness of high-throughput prediction of leaf ecophysiological traits using near infra-red spectroscopy and poro-fluorometry

Authors: Coindre, E., Boulord, R., Chir, L., Freitas, V., Ryckewaert, M., Laisne, T., Bouckenooghe, V., Lis, M., Cabrera-Bosquet, L., Doligez, A., Simonneau, T., Pallas, B., Coupel-Ledru, A., Segura, V.

Date: 2025-02-07 · Version: 1

DOI: 10.1101/2025.02.07.636194

Model Organism: Vitis vinifera

drought tolerance high-throughput phenotyping spectroscopy poro-fluorometry leaf mass per area

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