Learning oncogenetic networks by reducing to mixed integer linear programming.

ABSTRACT: Cancer can be a result of accumulation of different types of genetic mutations such as copy number aberrations. The data from tumors are cross-sectional and do not contain the temporal order of the genetic events. Finding the order in which the genetic events have occurred and progression pathways are of vital importance in understanding the disease. In order to model cancer progression, we propose Progression Networks, a special case of Bayesian networks, that are tailored to model disease progression. Progression networks have similarities with Conjunctive Bayesian Networks (CBNs) [1],a variation of Bayesian networks also proposed for modeling disease progression. We also describe a learning algorithm for learning Bayesian networks in general and progression networks in particular. We re

SUBMITTER: Shahrabi Farahani H 

PROVIDER: S-EPMC3683041 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature