Keywords: paralog processing, machine learning, transcriptomic analysis, Zea mays

Latest 43 Papers

Diploid gametes in maize by mutation of A-Type cyclins: a step towards apomeiosis and synthetic apomixis.

Authors: Skinner, D. J., Gaikwad, A. H., Fenner, J. A., Green, J., Cho, M.-J., Kelliher, T., Sundaresan, V.

Date: 2025-05-19 · Version: 1

DOI: 10.1101/2025.05.16.654085

Model Organism: Zea mays

synthetic apomixis diploid gametes Zea mays A-type cyclin tetraploid progeny

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

Metabolite profiling reveals slow and uncoordinated adjustment of C4 photosynthesis to sudden changes in irradiance

Authors: Arrivault, S., Medeiros, D. B., Sales, C. R. G., Guenther, M., Kromdijk, J., Fernie, A. R., Stitt, M.

Date: 2025-04-18 · Version: 1

DOI: 10.1101/2025.04.13.648559

Model Organism: Zea mays

C4 photosynthesis irradiance fluctuations metabolic buffering intercellular shuttles Zea mays

Large-scale single-cell profiling of stem cells uncovers redundant regulators of shoot development and yield trait variation

Authors: Xu, X., Passalacqua, M., Rice, B., Demesa-Arevalo, E., Kojima, M., Takebayashi, Y., Harris, B., Sakakibara, H., Gallavotti, A., Gillis, J., Jackson, D.

Date: 2025-04-17 · Version: 2

DOI: 10.1101/2024.03.04.583414

Model Organism: Multi-species

single-cell RNA sequencing shoot stem cells Arabidopsis thaliana Zea mays stem cell regulators

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

Volatiles from low R:FR-treated maize plants increase the emission of herbivore-induced plant volatiles in their neighbors

Authors: Escobar Bravo, R., Schimmel, B. C. J., Erb, M.

Date: 2025-04-02 · Version: 2

DOI: 10.1101/2024.10.05.616768

Model Organism: Zea mays

low R:FR light Zea mays herbivore‑induced plant volatiles volatile priming plant‑plant communication

High-resolution transcriptional atlas of growing maize shoot organs throughout plant development under well-watered and drought conditions

Authors: Zhang, J., Verbraeken, L., Sprenger, H., Mertens, S., Wuyts, N., Cannoot, B., De Block, J., Demuynck, K., Natran, A., Maleux, K., Merchie, J., Crafts-Brandner, S., Vogel, J., Bruce, W., Inze, D., Maere, S., Nelissen, H.

Date: 2025-03-13 · Version: 1

DOI: 10.1101/2025.03.12.642568

Model Organism: Zea mays

Zea mays leaf and internode development drought stress spatiotemporal transcriptome atlas gene regulatory networks

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

PreviousPage 4 of 5Next