Keywords: paralog processing, machine learning, ascorbate, lipidomics

Latest 18 Papers

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

Arabidopsis root lipid droplets are hubs for membrane homeostasis under heat stress, and triterpenoid synthesis and storage.

Authors: Scholz, P., Dabisch, J., Clews, A. C., Niemeyer, P. W., Vilchez, A. C., Lim, M. S. S., Sun, S., Hembach, L., Dreier, F., Blersch, K., Preuss, L., Bonin, M., Lesch, E., Iwai, Y., Shimada, T., Eirich, J., Finkemeier, I., Gutbrod, K., Doermann, P., Wang, Y., Mullen, R. T., Ischebeck, T.

Date: 2025-03-26 · Version: 1

DOI: 10.1101/2025.03.24.644787

Model Organism: Arabidopsis thaliana

lipid droplets heat stress Arabidopsis thaliana roots triterpene biosynthesis lipidomics

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

CDP-DAG synthases regulate plant growth and broad-spectrum disease resistance

Authors: Tan, R., Sha, G., Gong, Q., Yang, L., Wei, Y., Liu, X., Li, Y., Cheng, J., Du, X., Xue, H., Li, Q., Luo, J., Li, G.

Date: 2025-01-27 · Version: 1

DOI: 10.1101/2025.01.24.634677

Model Organism: Arabidopsis thaliana

Phosphatidic acid CDP‑DAG synthase Arabidopsis disease resistance lipidomics JA/SA signaling

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