Search Results for author: Alex Graudenzi

Found 9 papers, 4 papers with code

EAD: an ensemble approach to detect adversarial examples from the hidden features of deep neural networks

no code implementations24 Nov 2021 Francesco Craighero, Fabrizio Angaroni, Fabio Stella, Chiara Damiani, Marco Antoniotti, Alex Graudenzi

One of the key challenges in Deep Learning is the definition of effective strategies for the detection of adversarial examples.

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

1 code implementation13 Oct 2021 Fabrizio Angaroni, Marco Antoniotti, Alex Graudenzi

We introduce the Optimized Gillespie algorithm for simulating Stochastic sPAtial models of Cancer Evolution (OG-SPACE), a computational framework for the simulation of the spatio-temporal evolution of cancer subpopulations and of the experimental procedures of both bulk andsingle-cell sequencing.

Investigating the Compositional Structure Of Deep Neural Networks

no code implementations17 Feb 2020 Francesco Craighero, Fabrizio Angaroni, Alex Graudenzi, Fabio Stella, Marco Antoniotti

By defining a direct acyclic graph representing the composition of activation patterns through the network layers, it is possible to characterize the instances of the input data with respect to both the predicted label and the specific (linear) transformation used to perform predictions.

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

1 code implementation4 Sep 2017 Daniele Ramazzotti, Alex Graudenzi, Luca De Sano, Marco Antoniotti, Giulio Caravagna

Compared to other tools, TRaIT supports multi-region and single-cell sequencing data within the same statistical framework, and delivers expressive models that capture many complex evolutionary phenomena.

cyTRON and cyTRON/JS: two Cytoscape-based applications for the inference of cancer evolution models

2 code implementations8 May 2017 Lucrezia Patruno, Edoardo Galimberti, Daniele Ramazzotti, Giulio Caravagna, Luca De Sano, Marco Antoniotti, Alex Graudenzi

cyTRON was developed in Java; the code is available at https://github. com/BIMIB-DISCo/cyTRON and on the Cytoscape App Store http://apps. cytoscape. org/apps/cytron.

Efficient computational strategies to learn the structure of probabilistic graphical models of cumulative phenomena

no code implementations8 Mar 2017 Daniele Ramazzotti, Marco S. Nobile, Marco Antoniotti, Alex Graudenzi

In this work, we focus on a specific subclass of BNs, named Suppes-Bayes Causal Networks (SBCNs), which include specific structural constraints based on Suppes' probabilistic causation to efficiently model cumulative phenomena.

Modeling cumulative biological phenomena with Suppes-Bayes Causal Networks

no code implementations25 Feb 2016 Daniele Ramazzotti, Alex Graudenzi, Giulio Caravagna, Marco Antoniotti

Several diseases related to cell proliferation are characterized by the accumulation of somatic DNA changes, with respect to wildtype conditions.

Bayesian Inference Model Selection

PMCE: efficient inference of expressive models of cancer evolution with high prognostic power

2 code implementations26 Aug 2014 Fabrizio Angaroni, Kevin Chen, Chiara Damiani, Giulio Caravagna, Alex Graudenzi, Daniele Ramazzotti

Motivation: Driver (epi)genomic alterations underlie the positive selection of cancer subpopulations, which promotes drug resistance and relapse.

Proceedings Wivace 2013 - Italian Workshop on Artificial Life and Evolutionary Computation

no code implementations27 Sep 2013 Alex Graudenzi, Giulio Caravagna, Giancarlo Mauri, Marco Antoniotti

The Wivace 2013 Electronic Proceedings in Theoretical Computer Science (EPTCS) contain some selected long and short articles accepted for the presentation at Wivace 2013 - Italian Workshop on Artificial Life and Evolutionary Computation, which was held at the University of Milan-Bicocca, Milan, on the 1st and 2nd of July, 2013.

Artificial Life

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