Feb 25, 2016

Modeling cumulative biological phenomena with Suppes-Bayes causal networks

BioRxiv : the Preprint Server for Biology
Daniele RamazzottiGiulio Caravagna


Several statistical techniques have been recently developed for the inference of cancer progression models from the increasingly available NGS cross sectional mutational profiles. A particular algorithm, CAPRI, was proven to be the most efficient with respect to sample size and level of noise in the data. The algorithm combines structural constraints based on Suppes' theory of probabilistic causation and maximum likelihood fit with regularization,and defines constrained Bayesian networks, named Suppes-Bayes Causal Networks(SBCNs), which account for the selective advantage relations among genomic events. In general, SBCNs are effective in modeling any phenomenon driven by cumulative dynamics, as long as the modeled events are persistent. We here discuss on the effectiveness of the SBCN theoretical framework and we investigate the inference of: (i) the priors based on Suppes' theory and (ii) different maximum likelihood regularization parameters on the inference performance estimated on large synthetically generated datasets.

  • References
  • Citations


  • We're still populating references for this paper, please check back later.
  • References
  • Citations


  • This paper may not have been cited yet.

Mentioned in this Paper

Cancer Progression
TAPBP protein, human
Cell Proliferation
Statistical Technique
Clone Cells
Disease Progression

About this Paper

Related Feeds

BioRxiv & MedRxiv Preprints

BioRxiv and MedRxiv are the preprint servers for biology and health sciences respectively, operated by Cold Spring Harbor Laboratory. Here are the latest preprint articles (which are not peer-reviewed) from BioRxiv and MedRxiv.

Related Papers

Evolutionary Bioinformatics Online
Daniele RamazzottiMarco Antoniotti
BioRxiv : the Preprint Server for Biology
L. KolbowskiJuri Rappsilber
BioRxiv : the Preprint Server for Biology
Brian J SandersonM. S. Olson
© 2020 Meta ULC. All rights reserved