Nastasiya F. Grinberg

This study shows that polytunnel covering reduces Pseudomonas syringae dispersal and alters phyllosphere communities, while nitrogen fertigation improves plant growth and reduces disease severity in cherry orchards.

This study uses hyperspectral imaging and deep learning to classify multiple infections in wheat, achieving 81% accuracy for single and double infections.

This study investigates the tissue-specific roles of type III effectors and phytotoxins in Pseudomonas syringae pv. syringae pathogenicity to cherry, finding that toxins are critical for fruit infection while effector redundancy complicates disease progression.

This paper compares random forests and linear models for gene expression prediction in TWAS, finding RF outperforms linear methods and multi-tissue approaches improve results.

The paper identifies over 1300 transcription factor binding variants in CRMs associated with gene expression in human LCLs, revealing epromoters as dual-action regulatory elements.

This study identifies genetic variants with pleiotropic effects on cytokines and related traits using a multivariate genome-wide association analysis.

This paper evaluates machine learning methods for phenotype prediction in yeast, rice, and wheat, finding that standard ML methods often outperform classical statistical genetics approaches.

This paper introduces a Bayesian joint fine-mapping method that improves accuracy in identifying causal variants across related immune-mediated diseases by leveraging shared genetic information.