activity
20122021
most citedCAPRI: Efficient Inference of Cancer Progression Models from Cross-sectional Data

107 citations · 355 across the 19 of their papers we have counts for

collaborators

19 papers

cs.CV2021★ 1 cited

Unity is strength: Improving the Detection of Adversarial Examples with Ensemble Approaches

Francesco Craighero, Fabrizio Angaroni, Fabio Stella +3

A key challenge in computer vision and deep learning is the definition of robust strategies for the detection of adversarial examples. Here, we propose the adoption of ensemble app…

q-bio.PE2021

OG-SPACE: Optimized Stochastic Simulation of Spatial Models of Cancer Evolution

Fabrizio Angaroni, Marco Antoniotti, Alex Graudenzi

Algorithmic strategies for the spatio-temporal simulation of multi-cellular systems are crucial to generate synthetic datasets for bioinformatics tools benchmarking, as well as to…

cs.PL2020

Why You Cannot (Yet) Write an "Interval Arithmetic" Library in Common Lisp

Marco Antoniotti

"Interval Arithmetic" (IA) appears to be a useful numerical tool to have at hand in several applications. Alas, the current IA descriptions and proposed standards are always formul…

cs.LG2020★ 2 cited

Investigating the Compositional Structure Of Deep Neural Networks

Francesco Craighero, Fabrizio Angaroni, Alex Graudenzi +2

The current understanding of deep neural networks can only partially explain how input structure, network parameters and optimization algorithms jointly contribute to achieve the s…

q-bio.CB2017

Metabolic enrichment through functional gene rules

Davide Maspero, Claudio Isella, Marzia Di Filippo +6

It is well known that tumors originating from the same tissue have different prognosis and sensitivity to treatments. Over the last decade, cancer genomics consortia like the Cance…

q-bio.GN2017

Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data

Daniele Ramazzotti, Alex Graudenzi, Luca De Sano +2

Background. A large number of algorithms is being developed to reconstruct evolutionary models of individual tumours from genome sequencing data. Most methods can analyze multiple…