Keywords: paralog processing, transcriptomic analysis, machine learning, nutrient signaling

Latest 20 Papers

Molecular basis of delayed leaf senescence induced by short-term treatment with low phosphate in rice

Authors: Martin-Cardoso, H., Bundo, M., Garcia-Molina, A., San Segundo, B.

Date: 2026-01-24 · Version: 1

DOI: 10.64898/2026.01.23.701354

Model Organism: Oryza sativa

phosphate deficiency leaf senescence Oryza sativa CRISPR/Cas9 transcriptomic analysis

Successful Predictive Modeling of Pollen Fitness Phenotypes Is Enabled by Measures of Expression Specificity

Authors: Mueller, S. A. F., Vejlupkova, Z., Megraw, M., Fowler, J. E.

Date: 2026-01-13 · Version: 2

DOI: 10.64898/2025.12.05.692487

Model Organism: Zea mays

maize pollen phenotype prediction machine learning Ds-GFP insertional mutants expression specificity

METABOLIC AND TRANSCRIPTOMIC ANALYSES IDENTIFY COORDINATED RESOURCE REALLOCATION IN RESPONSE TO PHOSPHATE SUPPLY IN HEMP

Authors: Wee Y, B., Berkowitz, O., Ng, S., Pegg, A., Whelan, J., Jost, R.

Date: 2025-12-23 · Version: 2

DOI: 10.1101/2025.09.18.677093

Model Organism: Cannabis sativa

Cannabis sativa phosphate nutrition transcriptomics source‑sink regulation nutrient signaling

Key molecular and cellular events of table olive fruit abscission zone formation during natural maturation and after ethephon treatment

Authors: Wang, M., Santos, E., Eagle, S., Chernikova, A., Zhang, S., Tran, P., Marino, G., Jernstedt, J., Niederholzer, F., Wheeler-Dykes, B., Wilkop, T., Ferguson, L., Drakakaki, G.

Date: 2025-11-12 · Version: 1

DOI: 10.1101/2025.11.10.687704

Model Organism: Olea europaea

abscission zone Olea europaea transcriptomic analysis cell wall remodeling alkalinization

Evaluation of computational tools for predicting CRISPR gRNA on-target efficiency in plants.

Authors: Gong, Z., Chen, M., Zhang, H., Mortimer, J., Botella, J. R.

Date: 2025-10-15 · Version: 1

DOI: 10.1101/2025.10.15.679654

Model Organism: Multi-species

CRISPR guide RNA efficiency plant genome editing in silico prediction tools machine learning

Phylogenomic challenges in polyploid-rich lineages: Insights from paralog processing and reticulation methods using the complex genus Packera (Asteraceae: Senecioneae)

Authors: Moore-Pollard, E. R., Ellestad, P., Mandel, J.

Date: 2025-10-13 · Version: 2

DOI: 10.1101/2025.09.14.676143

Model Organism: Multi-species

phylogenomic discordance polyploidy paralog processing Packera historical biogeography

Antifungal activity and mechanism of limonene against Fusarium oxysporum, a pathogen of potato dry rot

Authors: Xia, Q., Dong, P.

Date: 2025-10-06 · Version: 1

DOI: 10.1101/2025.10.06.675995

Model Organism: Fusarium oxysporum

limonene Fusarium oxysporum potato dry rot bio‑based fungicide transcriptomic analysis

Usefulness of scRNA-seq data in predicting plant metabolic pathway genes

Authors: Ma, J., Wang, Z., Zou, L., Wang, X., Zuo, X., Wang, F., Wang, Z., Li, Z., Li, L., Wang, P.

Date: 2025-10-02 · Version: 2

DOI: 10.1101/2024.10.07.617125

Model Organism: Arabidopsis thaliana

metabolic pathway genes single-cell RNA sequencing machine learning co-expression networks prediction accuracy

TPS Proteins coordinate plant growth with sugar availability via the SnRK1 Kinase

Authors: Reis-Barata, D., Confraria, A., Margalha, L., Peixoto, B., Lopes, F. L., Belda-Palazon, B., Jurke, B., Arrivault, S., Shukla, V., Feil, R., Licausi, F., Kaschani, F., Kaiser, M., Caldana, C., Stitt, M., Lunn, J. E., Baena-Gonzalez, E.

Date: 2025-08-20 · Version: 1

DOI: 10.1101/2025.08.17.670254

Model Organism: Arabidopsis thaliana

trehalose 6-phosphate (T6P)SnRK1 kinase TPS5/6/7 proteins Arabidopsis thaliana nutrient signaling

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

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