Keywords: decoding modules, machine learning, transcriptome, transcriptome analysis, ascorbate

Latest 22 Papers

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

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

Physiological characterization in a somaclonal mutant for fruit shape derived from persimmon 'Hiratanenashi'

Authors: Horiuchi, A., Matsuzaki, R., Onoue, N., Ushijima, K., Kubo, Y., Akagi, T., Minamikawa, M. F.

Date: 2025-05-19 · Version: 1

DOI: 10.1101/2025.05.19.654774

Model Organism: Diospyros kaki

fruit shape persimmon (Diospyros kaki)elliptic Fourier descriptor transcriptome analysis WOX13

Arabidopsis lines with modified ascorbate concentrations reveal a link between ascorbate and auxin biosynthesis

Authors: Fenech, M., Zulian, V., Moya-Cuevas, J., Arnaud, D., Morilla, I., Smirnoff, N., Botella, M. A., Stepanova, A. N., Alonso, J. M., Martin-Pizarro, C., Amorim-Silva, V.

Date: 2025-05-16 · Version: 1

DOI: 10.1101/2025.05.15.654287

Model Organism: Arabidopsis thaliana

ascorbate Arabidopsis thaliana auxin biosynthesis redox homeostasis transcriptomics

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

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

MYB93 regulates responses to environmental sulphur in Arabidopsis and tomato.

Authors: Cao, X., Wilkinson, H. B., Hutton, B., McMulkin, N., Graham, N. S., Etherington, R., Oliver, A. G., Clayton, C. M., Kaur, H., Coates, J. C.

Date: 2025-03-07 · Version: 1

DOI: 10.1101/2025.03.04.641362

Model Organism: Arabidopsis thaliana

sulfur nutrition AtMYB93 root development transcriptome analysis elemental analysis

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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