Keywords: paralog processing, machine learning, Botrytis cinerea

Latest 22 Papers

Predicting flowering time using integrated morphophysiological and genomic data with machine learning models

Authors: Babaei, M., Nemati, H., Arouiee, H., Torkamaneh, D.

Date: 2025-08-13 · Version: 1

DOI: 10.1101/2025.08.11.669697

Model Organism: Cannabis sativa

flowering time Cannabis sativa genotyping-by-sequencing machine learning SNP markers

Unravelling Induced Resistance in Strawberry: Distinct Metabolomic Signatures Define Cultivar-Specific Resistance to Botrytis cinerea

Authors: Murena, C., Pastor, V., Fernandes, T. R., Carvalho, S. M. P., Luna, E.

Date: 2025-08-01 · Version: 1

DOI: 10.1101/2025.07.29.667391

Model Organism: Fragaria x ananassa

Botrytis cinerea induced resistance Fragaria x ananassa elicitor treatment untargeted metabolomics

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

Alginate Oligosaccharides (from DP2 to DP9) Differentially Modulate Phytohormone Levels in Botrytis cinerea-Infected Wheat

Authors: Zhang, Z., Wang, X., Chi, Y.

Date: 2025-06-20 · Version: 1

DOI: 10.1101/2025.06.18.660292

Model Organism: Triticum aestivum

alginate oligosaccharides degree of polymerization phytohormone modulation wheat Botrytis cinerea

The Vitis vinifera receptor VvLYK6 negatively regulates chitin-triggered immune responses and promotes fungal infections

Authors: VILLETTE, J., MARZARI, T., LANDRY, D., ROUDAIRE, T., ROMANET, R., KLINGUER, A., LEBORGNE-CASTEL, N., VICEDO, C., GASCIOLLI, V., POUZET, C., LEFEBVRE, B., HELOIR, M. C., POINSSOT, B.

Date: 2025-06-09 · Version: 1

DOI: 10.1101/2025.06.06.658283

Model Organism: Arabidopsis thaliana

VvLYK6 LysM receptor-like kinase chitin-triggered immunity Botrytis cinerea susceptibility gene

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

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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