Keywords: paralog processing, machine learning, barley

Latest 20 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

New molecular players: siRNA expression and gene regulation in Hordeum vulgare ageing seeds after germination

Authors: Puchta-Jasinska, M., Bolc, P., Motor, A., Boczkowska, M.

Date: 2025-08-01 · Version: 1

DOI: 10.1101/2025.07.28.667253

Model Organism: Hordeum vulgare

small interfering RNAs seed viability barley RNA‑directed DNA methylation trans‑acting siRNAs

Post-Domestication selection of MKK3 Shaped Seed Dormancy and End-Use Traits in Barley

Authors: Jorgensen, M. E., Vequaud, D., Wang, Y., Andersen, C. B., Bayer, M., Box, A., Braune, K., Cai, Y., Chen, F., Antonio Cuesta-Seijo, J., Dong, H., Fincher, G. B., Gojkovic, Z., Huang, Z., Jaegle, B., Kale, S. M., Krsticevic, F., Roux, P.-M. L., Lozier, A., Lu, Q., Mascher, M., Murozuka, E., Nakamura, S., Simmelsgaard, M. U., Pedas, P. R., Pin, P., Sato, K., Spannagl, M., Rasmussen, M. W., Russell, J., Schreiber, M., Thomsen, H. C., Tulloch, S., Thomsen, N. W., Voss, C., Skadhauge, B., Stein, N., Waugh, R., Willerslev, E., Dockter, C.

Date: 2025-07-16 · Version: 1

DOI: 10.1101/2025.07.11.664137

Model Organism: Hordeum vulgare

grain dormancy pre-harvest sprouting MKK3 MAPK pathway barley

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

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

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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