Keywords: paralog processing, machine learning, local adaptation

Latest 19 Papers

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

Extensive photophysiological variation in wild barley is linked to environmental origin

Authors: Breil-Aubert, M., Shaw, K., Royles, J., Rodrigues Gabriel Sales, C., Walter, J., Taylor, G., Vath, R., Bdolach, E., Dev Tiwari, L., Mathan, J., Lawson, T., Fridman, E., Kromdijk, J., Ferguson, J. N.

Date: 2025-03-31 · Version: 1

DOI: 10.1101/2025.03.28.645825

Model Organism: Hordeum vulgare

crop wild relatives photophysiology genome-wide association study local adaptation stomatal conductance

Phytochrome A is required for light-inhibited germination of Aethionema arabicum seed

Authors: Merai, Z., Xu, F., Hajdu, A., Kozma-Bognar, L., Dolan, L.

Date: 2025-02-28 · Version: 1

DOI: 10.1101/2025.02.26.640300

Model Organism: Aethionema arabicum

negative photoblastic germination phytochrome A Aethionema arabicum high‑irradiance response local adaptation

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

Transcriptome differentiation in Cryptomeria japonica trees with different origins growing in the north and south of Japan

Authors: Ujino-Ihara, T., Uchiyama, K., Kanetani, S., Suyama, Y., Tsumura, Y.

Date: 2025-02-25 · Version: 1

DOI: 10.1101/2025.02.22.639673

Model Organism: Cryptomeria japonica

local adaptation transcriptome analysis weighted gene co‑expression network analysis (WGCNA)SNP differentiation defense response genes

Remote sensing reveals inter- and intraspecific variation in riparian cottonwood (Populus spp) response to drought

Authors: Seeley, M., Wiebe, B. C., Gehring, C. A., Hultine, K., Posch, B. C., Cooper, H. F., Schaefer, E. A., Bock, B. M., Abraham, A. J., Moran, M. E., Keith, A., Allan, G. J., Scull, M., Whitham, T. G., Martin, R. E., Asner, G. P., Doughty, C.

Date: 2025-02-19 · Version: 1

DOI: 10.1101/2025.02.17.638728

Model Organism: Populus fremontii

drought response leaf-level VSWIR spectroscopy UAV thermal imaging genotype-by-environment interaction local adaptation

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