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Found 9 papers, 4 papers with code
Unity is strength: Improving the Detection of Adversarial Examples with Ensemble Approaches
A key challenge in computer vision and deep learning is the definition of robust strategies for the detection of adversarial examples.
OG-SPACE: Optimized Stochastic Simulation of Spatial Models of Cancer Evolution
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
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
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
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.
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.
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.
Proceedings Wivace 2013 - Italian Workshop on Artificial Life and Evolutionary Computation
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.
