Search Results for author: Daniele Ramazzotti

Found 17 papers, 5 papers with code

Probabilistic Causal Analysis of Social Influence

no code implementations6 Aug 2018 Francesco Bonchi, Francesco Gullo, Bud Mishra, Daniele Ramazzotti

Experiments on synthetic data show that our method is able to retrieve the genuine causal arcs w. r. t.

Improved survival of cancer patients admitted to the ICU between 2002 and 2011 at a U.S. teaching hospital

no code implementations6 Aug 2018 Chris Sauer, Jinghui Dong, Leo Celi, Daniele Ramazzotti

MIMIC-III, a freely accessible critical care database of Beth Israel Deaconess Medical Center, Boston, USA was used to retrospectively study trends and outcomes of cancer patients admitted to the ICU between 2002 and 2011.

Withholding or withdrawing invasive interventions may not accelerate time to death among dying ICU patients

no code implementations4 Aug 2018 Daniele Ramazzotti, Peter Clardy, Leo Anthony Celi, David J. Stone, Robert S. Rudin

Comparing the period 2002-2005 vs. 2008-2011, we found a reduction in the use of vasopressors and inotropes among patients with the lowest severity who died within 30 days of ICU admission (41. 8 vs. 36. 2 hours, p<0. 001) but not among those with the highest severity.

Investigating the performance of multi-objective optimization when learning Bayesian Networks

no code implementations3 Aug 2018 Paolo Cazzaniga, Marco S. Nobile, Daniele Ramazzotti

Bayesian Networks have been widely used in the last decades in many fields, to describe statistical dependencies among random variables.

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.

Learning the structure of Bayesian Networks via the bootstrap

1 code implementation7 Jun 2017 Giulio Caravagna, Daniele Ramazzotti

Learning the structure of dependencies among multiple random variables is a problem of considerable theoretical and practical interest.

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.

SIMLR: A Tool for Large-Scale Genomic Analyses by Multi-Kernel Learning

1 code implementation21 Mar 2017 Bo Wang, Daniele Ramazzotti, Luca De Sano, Junjie Zhu, Emma Pierson, Serafim Batzoglou

We here present SIMLR (Single-cell Interpretation via Multi-kernel LeaRning), an open-source tool that implements a novel framework to learn a sample-to-sample similarity measure from expression data observed for heterogenous samples.

Clustering Dimensionality Reduction

Parallel Implementation of Efficient Search Schemes for the Inference of Cancer Progression Models

no code implementations8 Mar 2017 Daniele Ramazzotti, Marco S. Nobile, Paolo Cazzaniga, Giancarlo Mauri, Marco Antoniotti

The emergence and development of cancer is a consequence of the accumulation over time of genomic mutations involving a specific set of genes, which provides the cancer clones with a functional selective advantage.


Causal Data Science for Financial Stress Testing

no code implementations8 Mar 2017 Gelin Gao, Bud Mishra, Daniele Ramazzotti

The most recent financial upheavals have cast doubt on the adequacy of some of the conventional quantitative risk management strategies, such as VaR (Value at Risk), in many common situations.


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

A Model of Selective Advantage for the Efficient Inference of Cancer Clonal Evolution

no code implementations15 Feb 2016 Daniele Ramazzotti

The motivations are manifold: (i) growing NGS and single cell data from cancer patients, (ii) need for novel Data Science and Machine Learning algorithms to infer models of cancer progression, and (iii) a desire to understand the temporal and heterogeneous structure of tumor to tame its progression by efficacious therapeutic intervention.

Learning Theory

Exposing the Probabilistic Causal Structure of Discrimination

no code implementations2 Oct 2015 Francesco Bonchi, Sara Hajian, Bud Mishra, Daniele Ramazzotti

Discrimination discovery from data is an important task aiming at identifying patterns of illegal and unethical discriminatory activities against protected-by-law groups, e. g., ethnic minorities.

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.

Cannot find the paper you are looking for? You can Submit a new open access paper.