Viola Fanfani

This paper introduces Giraffe, a matrix factorization method for gene regulatory network inference that estimates signed regulatory effects, showing improved accuracy in distinguishing activating and inhibitory interactions across multiple biological contexts.

The paper identifies sex-biased regulation of cancer genes in normal tissues, linking these differences to sex-specific cancer incidence patterns.

This study identifies aging-related changes in gene regulatory networks linked to LUAD risk, prognosis, and therapy response using multi-omics data from GTEx and TCGA.

The paper identifies sex differences in lung adenocarcinoma by integrating multi-omics data to discover sex-biased signaling pathways and potential sex-specific therapies.

The paper develops a Nextflow workflow for reproducibly inferring regulatory networks from TCGA data, showing its application to colon cancer subtypes.

This study investigates how loss of the Y chromosome in lung adenocarcinoma affects the tumor immune environment and response to immunotherapy.

BONOBO infers sample-specific co-expression networks to capture individual heterogeneity in gene regulation, outperforming existing methods in yeast, breast cancer, and thyroid tissue analyses.

This paper introduces node2vec2rank, a method for graph differential analysis that identifies biologically relevant differences between weighted networks in cancer subtypes and single-cell dynamics.

PHOENIX uses biologically informed neural ODEs to infer genome-wide regulatory dynamics, demonstrating scalability and interpretability in simulated and real data.

This paper synthesizes the largest eukaryotic yeast chromosome, synIV, and investigates how its 3D structure affects gene regulation and transcription.

The paper uses CRISPR D-BUGS and chromosome swapping to identify and resolve genetic interactions in synthetic chromosomes, revealing unexpected links between transcription, inositol metabolism, and tRNA abundance.

The paper constructs a synthetic yeast chromosome XI and demonstrates how non-coding DNA modifications affect cell fitness, enabling on-demand extrachromosomal circular DNA formation.

The paper introduces netZoo, an open-source software suite for analyzing biological networks, demonstrating its use on cancer cell line data to identify drug resistance and cancer development factors.

This paper introduces SBM-GNN, a graph neural network model that identifies cancer driver genes and pathways from omic data, outperforming existing methods and revealing key cancer-related processes.

The paper introduces a statistical framework for analyzing geneset networks and implements it in an open-source tool called PyGNA.

This study uses a new method to analyze gene-level heritability in cancer, identifying genes that explain significant heritability and are involved in tumorigenesis.