124 citations · 588 across the 20 of their papers we have counts for
23 papers
Probabilistic Causal Analysis of Social Influence
Francesco Bonchi, Francesco Gullo, Bud Mishra +1
Mastering the dynamics of social influence requires separating, in a database of information propagation traces, the genuine causal processes from temporal correlation, i.e., homop…
Improved survival of cancer patients admitted to the ICU between 2002 and 2011 at a U.S. teaching hospital
Chris Sauer, Jinghui Dong, Leo Celi +1
Over the past decades, both critical care and cancer care have improved substantially. Due to increased cancer-specific survival, we hypothesized that both the number of cancer pat…
Withholding or withdrawing invasive interventions may not accelerate time to death among dying ICU patients
Daniele Ramazzotti, Peter Clardy, Leo Anthony Celi +2
We considered observational data available from the MIMIC-III open-access ICU database and collected within a study period between year 2002 up to 2011. If a patient had multiple a…
Investigating the performance of multi-objective optimization when learning Bayesian Networks
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. In general, learning the structure of such…
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…
Learning the structure of Bayesian Networks via the bootstrap
Giulio Caravagna, Daniele Ramazzotti
Learning the structure of dependencies among multiple random variables is a problem of considerable theoretical and practical interest. Within the context of Bayesian Networks, a p…
cyTRON and cyTRON/JS: two Cytoscape-based applications for the inference of cancer evolution models
Lucrezia Patruno, Edoardo Galimberti, Daniele Ramazzotti +4
The increasing availability of sequencing data of cancer samples is fueling the development of algorithmic strategies to investigate tumor heterogeneity and infer reliable models o…
Learning the structure of Bayesian Networks: A quantitative assessment of the effect of different algorithmic schemes
Stefano Beretta, Mauro Castelli, Ivo Goncalves +2
One of the most challenging tasks when adopting Bayesian Networks (BNs) is the one of learning their structure from data. This task is complicated by the huge search space of possi…
Combining Bayesian Approaches and Evolutionary Techniques for the Inference of Breast Cancer Networks
Stefano Beretta, Mauro Castelli, Ivo Goncalves +2
Gene and protein networks are very important to model complex large-scale systems in molecular biology. Inferring or reverseengineering such networks can be defined as the process…
Parallel Implementation of Efficient Search Schemes for the Inference of Cancer Progression Models
Daniele Ramazzotti, Marco S. Nobile, Paolo Cazzaniga +2
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…
SIMLR: A Tool for Large-Scale Genomic Analyses by Multi-Kernel Learning
Bo Wang, Daniele Ramazzotti, Luca De Sano +3
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 f…
Causal Data Science for Financial Stress Testing
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…
Efficient computational strategies to learn the structure of probabilistic graphical models of cumulative phenomena
Daniele Ramazzotti, Marco S. Nobile, Marco Antoniotti +1
Structural learning of Bayesian Networks (BNs) is a NP-hard problem, which is further complicated by many theoretical issues, such as the I-equivalence among different structures.…
A Model of Selective Advantage for the Efficient Inference of Cancer Clonal Evolution
Daniele Ramazzotti
Recently, there has been a resurgence of interest in rigorous algorithms for the inference of cancer progression from genomic data. The motivations are manifold: (i) growing NGS an…
Modeling cumulative biological phenomena with Suppes-Bayes Causal Networks
Daniele Ramazzotti, Alex Graudenzi, Giulio Caravagna +1
Several diseases related to cell proliferation are characterized by the accumulation of somatic DNA changes, with respect to wildtype conditions. Cancer and HIV are two common exam…
Exposing the Probabilistic Causal Structure of Discrimination
Francesco Bonchi, Sara Hajian, Bud Mishra +1
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., eth…
Algorithmic Methods to Infer the Evolutionary Trajectories in Cancer Progression
Giulio Caravagna, Alex Graudenzi, Daniele Ramazzotti +6
The genomic evolution inherent to cancer relates directly to a renewed focus on the voluminous next generation sequencing (NGS) data, and machine learning for the inference of expl…
TRONCO: an R package for the inference of cancer progression models from heterogeneous genomic data
Luca De Sano, Giulio Caravagna, Daniele Ramazzotti +4
Motivation: We introduce TRONCO (TRanslational ONCOlogy), an open-source R package that implements the state-of-the-art algorithms for the inference of cancer progression models fr…
An Observational Study: The Effect of Diuretics Administration on Outcomes of Mortality and Mean Duration of I.C.U. Stay
Daniele Ramazzotti
This thesis conducts an observational study into whether diuretics should be administered to ICU patients with sepsis when length of stay in the ICU and 30-day post-hospital mortal…
PMCE: efficient inference of expressive models of cancer evolution with high prognostic power
Fabrizio Angaroni, Kevin Chen, Chiara Damiani +3
Motivation: Driver (epi)genomic alterations underlie the positive selection of cancer subpopulations, which promotes drug resistance and relapse. Even though substantial heterogene…
CAPRI: Efficient Inference of Cancer Progression Models from Cross-sectional Data
Daniele Ramazzotti, Giulio Caravagna, Loes Olde Loohuis +5
We devise a novel inference algorithm to effectively solve the cancer progression model reconstruction problem. Our empirical analysis of the accuracy and convergence rate of our a…
Inferring tree causal models of cancer progression with probability raising
Loes Olde Loohuis, Giulio Caravagna, Alex Graudenzi +4
Existing techniques to reconstruct tree models of progression for accumulative processes, such as cancer, seek to estimate causation by combining correlation and a frequentist noti…
A Model of Colonic Crypts using SBML Spatial
Daniele Ramazzotti, Carlo Maj, Marco Antoniotti
The Spatial Processes package enables an explicit definition of a spatial environment on top of the normal dynamic modeling SBML capabilities. The possibility of an explicit repres…