Francesca Meteora Buffa

PHAROS uses a pretrained single-cell perturbation model to predict and prioritize effective drug combinations without retraining, showing success in cell lines and patient-derived tumors.

This study examines scaling laws in single-cell RNA sequencing foundation models, showing how performance scales with model size and training compute.

pysigscore scores gene signatures in bulk and single-cell RNA-seq data, validating its performance on multiple datasets.

DNA2Graph automates the identification of non-linear DNA molecules in electron microscopy images, significantly reducing the need for manual review and improving structure detection.

GENESIS converts Multiome GEX data into synthetic scRNA-Seq profiles using generative models, improving cell-type classification accuracy.

The paper uses multiomics analysis of 2D and 3D hiPSC-CMs under insulin resistance conditions to show metabolic and contractile dysfunction resembling diabetic cardiomyopathy.

This paper uses agent-based modeling to study metabolic cooperation between oxygenated and hypoxic tumor cells, finding that aerobic glycolysis provides an overall advantage and that blocking glucose transporters is more effective than lactate transporters in reducing tumor growth.

Ingres fits probabilistic Boolean networks to single-cell RNA-seq data, capturing cell phenotype differences using continuous protein activity measures.

The paper uses network modeling to analyze how TP53 mutations affect gene regulation across cancer types and samples.

The paper identifies a switch from DICER-dependent to Argonaute-mediated miRNA biogenesis in cancer, showing that non-canonical processing of oncogenic miRNAs promotes cancer progression.

The paper shows that inherited broken chromosomes in S. pombe undergo cycles of replication and processing, leading to chromosomal instability and rearrangements.

The paper identifies heritable genetic variants in cancer genes that associate with both cancer risk and anthropometric traits in a large cohort.

The paper introduces BioSWITCH, a tool that converts static gene regulatory networks into executable models to predict cellular behavior.

This paper introduces a modelling framework that links genotype, signalling networks, and cell behaviour in a 3D microenvironment to predict single- and multi-cellular dynamics.

The paper shows how non-coding RNA acting as miRNA sponges can generate oscillatory dynamics in a transcriptional feedback network, with implications for understanding RNA species roles in biological processes.

The paper investigates miRNA regulation across 15 cancer types, finding complex interactions between miRNA expression and cancer hallmarks, with specific emphasis on the oncomiR-1 cluster and repression of tumour suppressor genes.

This paper introduces sigQC, an R package that standardizes gene signature quality control, demonstrating its application in evaluating signatures on cancer gene expression data.