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

  title={PMCE: efficient inference of expressive models of cancer evolution with high prognostic power},
  author={Fabrizio Angaroni and Kevin Chen and Chiara Damiani and Giulio Caravagna and Alex Graudenzi and Daniele Ramazzotti},
Motivation Driver (epi)genomic alterations underlie the positive selection of cancer subpopulations, which promotes drug resistance and relapse. Even though substantial heterogeneity is witnessed in most cancer types, mutation accumulation patterns can be regularly found and can be exploited to reconstruct predictive models of cancer evolution. Yet, available methods cannot infer logical formulas connecting events to represent alternative evolutionary routes or convergent evolution. Results We… 

EvAM-Tools: tools for evolutionary accumulation and cancer progression models

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J-SPACE: a Julia package for the simulation of spatial models of cancer evolution and of sequencing experiments

A Julia package for SPAtial Cancer Evolution (J-SPACE), which allows one to model and simulate a broad set of experimental scenarios, phenomenological rules and sequencing settings, and is designed to allow the performance assessment of downstream bioinformatics pipelines processing NGS data.



Estimating the predictability of cancer evolution

A computational method based on conjunctive Bayesian networks (CBNs) to quantify the predictability of cancer evolution directly from mutational data, without the need for measuring or estimating fitness is developed.

Efficient sampling for Bayesian inference of conjunctive Bayesian networks

A Bayesian inference scheme for Conjunctive Bayesian Networks, a probabilistic graphical model in which mutations accumulate according to partial order constraints and cancer genotypes are observed subject to measurement noise is presented.

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The integration of genomewide data from multiple platforms delineated three molecular classes of lower-grade gliomas that were more concordant with IDH, 1p/19q, and TP53 status than with histologic class.

Probabilistic Graphical Models - Principles and Techniques

The framework of probabilistic graphical models, presented in this book, provides a general approach for causal reasoning and decision making under uncertainty, allowing interpretable models to be constructed and then manipulated by reasoning algorithms.

Quantifying cancer progression with conjunctive Bayesian networks

This work presents a specific probabilistic graphical model for the accumulation of mutations and their interdependencies in cancer progression that can be used to improve genetics-based survival predictions which could support diagnostics and prognosis.

Estimating the predictability of cancer

  • evolution. Bioinformatics 35,
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The theoretical foundations of this approach are discussed and the influence on the model selection task of (1) the poset based on Suppes’ theory and (2) different regularization strategies are investigated.

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