Isabella Castiglioni

The paper uses variant calling from scRNA-seq data to identify potential sample swaps between patient-derived cell lines.

This paper introduces LACE, a framework for analyzing longitudinal single-cell mutation data to model cancer evolution and therapy response.

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.

Moonlight identifies cancer driver genes and their roles by integrating functional analysis and gene regulatory networks from expression data, distinguishing tumor suppressors, oncogenes, and moonlight genes.

The paper examines functional brain network dynamics during a working memory task, finding a two-phase pattern of segregation and integration that differs between correct and incorrect trials.