Search Results for author:
Found 17 papers, 5 papers with code
Probabilistic Causal Analysis of Social Influence
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
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
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
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
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
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
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.
Learning the structure of Bayesian Networks: A quantitative assessment of the effect of different algorithmic schemes
One of the most challenging tasks when adopting Bayesian Networks (BNs) is the one of learning their structure from data.
SIMLR: A Tool for Large-Scale Genomic Analyses by Multi-Kernel Learning
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.
Combining Bayesian Approaches and Evolutionary Techniques for the Inference of Breast Cancer Networks
Gene and protein networks are very important to model complex large-scale systems in molecular biology.
Parallel Implementation of Efficient Search Schemes for the Inference of Cancer Progression Models
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
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
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
Several diseases related to cell proliferation are characterized by the accumulation of somatic DNA changes, with respect to wildtype conditions.
A Model of Selective Advantage for the Efficient Inference of Cancer Clonal Evolution
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.
Exposing the Probabilistic Causal Structure of Discrimination
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
Motivation: Driver (epi)genomic alterations underlie the positive selection of cancer subpopulations, which promotes drug resistance and relapse.
