Silvia Bottini

The paper identifies and characterizes orphan genes in eight root-knot nematode species, finding they contribute to parasitism and are expressed during the infective juvenile stage.

HIVE integrates single-stress transcriptomics data to identify gene signatures responding to multifactorial stress combinations in plants.

VIOLA prioritizes pathogenic variants in mitochondrial diseases using a variational autoencoder and outlier detection, outperforming existing methods in a patient cohort.

The paper constructs a knowledge graph and uses topological data analysis to uncover the gene regulatory network of tomato's response to multiple pathogens, identifying key transcription factors and regulatory hubs.

The paper introduces MOnSTER, a tool for clustering and scoring protein sequence motifs, and applies it to identify specific motifs in plant-parasitic nematode effectors.

The paper introduces LEAPH, a machine learning tool for predicting phytoplasma effector proteins, and EffectorComb, an app for analyzing these predictions, identifying 2089 putative effectors across 13 proteomes.

This paper benchmarks feature selection and extraction methods to enhance machine-learning performance in patient classification using metabolomics data.