Ian Morilla

The paper trains a domain-specific plant peptide language model and compares its performance to generalist models, finding domain pretraining provides significant efficiency gains.

The paper characterizes tomato fruit RALF peptides, showing structural and functional differences among SlRALF5, SlRALF7, and SlRALF10, with distinct roles in growth and cell wall signaling.

The paper uses multimodal analysis to uncover how fungal symbionts reprogram plant sensory systems through coordinated transcriptional, metabolic, and phenotypic responses, revealing novel integration hubs and mechanisms.

S2-PepAnalyst predicts plant small signalling peptides with high accuracy using machine learning and integrates plant-specific datasets.

The paper investigates the relationship between ascorbate levels and auxin biosynthesis in Arabidopsis by analyzing mutant lines with altered ascorbate concentrations.

The paper models the plasma proteome of COVID-19 patients to identify proteins associated with disease severity using a graph convolutional network.

The paper models inflammatory bowel disease (IBD) status as Riemannian manifolds using proteomic data to identify module regulators as singularities and reinterpret disease dynamics as compensatory responses.