Loredana Martignetti

scROMA infers pathway activity from single-cell transcriptomics data, accounting for batch effects, and provides a simulation framework for benchmarking.

This paper introduces a graph-based topic modelling method for single-cell multi-omic data, showing improved performance in clustering and biological interpretation.

NeKo automates biological network construction by integrating molecular interactions from databases, enabling efficient network building for research applications.

The paper constructs a CF signalling network from transcriptomic data to identify dysregulated proteins and potential therapeutic targets in Cystic Fibrosis.

This study uses topic modeling to cluster breast cancer single-cell data and identify gene signatures, improving interpretability and accuracy in distinguishing drug-sensitive and resistant cell types.

The paper introduces MoPc, a tool that predicts miRNA-target interactions by analyzing multi-omic data, and validates its results on cancer datasets.

The paper introduces rROMA, a software package for computing gene set activity from omics data, and applies it to cystic fibrosis transcriptomic data to identify disease-related pathways and potential biases.

Cell-ID extracts per-cell gene signatures from single-cell sequencing data to enable unbiased cell identity recognition across diverse contexts.

The paper investigates how genomic alterations affect molecular networks in prostate cancer by integrating genomic, transcriptomic, and proteomic data from 105 samples, identifying a sub-network predictive of tumor aggressiveness and recurrence.

This study rapidly generates proteomic data for NCI-60 cancer cell lines and identifies drug response determinants by integrating proteomic, genetic, and transcriptomic data.

The paper identifies miRNA clusters driving triple negative breast cancer subtypes using a new computational pipeline and validates their role in promoting the phenotype through proliferation and EMT control.

The paper defines a set of informative gene signatures from pan-cancer data and introduces InfoSigMap to visualize their compositional and functional redundancies.