Keywords: paralog processing, machine learning, Triticum aestivum

Latest 24 Papers

Real-Time Quantitative PCR Method for Assessing Wheat Cultivars for Resistance to Zymoseptoria tritici

Authors: Adhikari, T. B., Balaji, B., Goodwin, S. B.

Date: 2025-04-06 · Version: 1

DOI: 10.1101/2025.04.02.646880

Zymoseptoria tritici Triticum aestivum real-time qPCR fungal DNA quantification Stb4/Stb8 resistance

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

TaWUS2D regulates the number of grains per spikelet by enhancing the number of fertile ovaries in Multi-Ovary Wheat

Authors: Schoen, A. W., Sharma, P. K., Mahlandt, A., Chen, A., Sheng, H., Kochian, L., Gao, P., Xiang, D., Quilichini, T., Venglat, P., Wang, S., Yadav, I. S., Gu, Y., Rodriguez-Leal, D., Luo, W., Qi, Y., Meier, N., Kajla, A., Willman, M., Brown-Guedira, G., Youngblood, C., Hulse-Kemp, A., Murphy, A., Gill, B., Uauy, C., Datla, R., Rawat, N., Tiwari, V.

Date: 2025-02-08 · Version: 1

DOI: 10.1101/2025.02.05.636547

Model Organism: Triticum aestivum

Triticum aestivum multi-ovary phenotype TaWUS2D map-based cloning HiFi long‑read genome assembly

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