Keywords: paralog processing, ascorbate, machine learning, ELF3

Latest 18 Papers

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

Early Flowering 3 (ELF3) inhibits hypocotyl phototropism in light-grown Arabidopsis seedlings.

Authors: Cobb, G., Krahmer, J., Nawkar, G. M., Boccaccini, A., Fankhauser, C.

Date: 2025-04-15 · Version: 1

DOI: 10.1101/2025.04.09.647958

Model Organism: Arabidopsis thaliana

phototropism ELF3 Evening Complex PIF transcription factors circadian regulation

Data-driven mathematical modelling explains altered timing of EARLY FLOWERING 3 in the wheat circadian oscillator

Authors: Upadhyay, A., Rowland-Chandler, J., Stewart-Wood, J., Pingarron-Cardenas, G., Tokuda, I. T., Webb, A. A., Locke, J. C.

Date: 2025-04-08 · Version: 1

DOI: 10.1101/2025.04.08.644541

Model Organism: Triticum aestivum

circadian rhythm ELF3 wheat TOC1 repression computational modeling

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

ELF3 controls trait heterogeneity by tuning the rate of maturation in Arabidopsis and barley

Authors: McCarthy, K., Pook, H., Redmond, E. J., Ronald, J., Keir, J., Zhu, Z., Quint, M., Davis, P. A., Hartley, S. E., Davis, S., Ezer, D.

Date: 2025-03-26 · Version: 1

DOI: 10.1101/2025.03.24.644900

Model Organism: Multi-species

ELF3 trait heterogeneity developmental timing bet-hedging osmotic stress resilience

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

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