In this paper, we present the disjunctive Bayesian network (DBN), a novel oncogenetic model with a phylogenetic interpretation. DBN is expressive enough to capture cancer subtypes' trajectories and mutually exclusive relations between alterations from unstratified data.
AutoMeKin2021 is an updated version of tsscds2018, a program for the automated discovery of reaction mechanisms (
- NSF-PAR ID:
- 10449867
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Journal of Computational Chemistry
- Volume:
- 42
- Issue:
- 28
- ISSN:
- 0192-8651
- Page Range / eLocation ID:
- p. 2036-2048
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Motivation : Cancer is the process of accumulating genetic alterations that confer selective advantages to tumor cells. The order in which aberrations occur is not arbitrary, and inferring the order of events is challenging due to the lack of longitudinal samples from tumors. Moreover, a network model of oncogenesis should capture biological facts such as distinct progression trajectories of cancer subtypes and patterns of mutual exclusivity of alterations in the same pathways.Results : In cases where the number of studied alterations is small (), we provide an efficient dynamic programming implementation of an exact structure learning method that finds a best DBN in the superexponential search space of networks. In rare cases that the number of alterations is large, we provided an efficient genetic algorithm in our software package, OncoBN. Through numerous synthetic and real data experiments, we show OncoBN's ability in inferring ground truth networks and recovering biologically meaningful progression networks.Availability : OncoBN is implemented in R and is available athttps://github.com/phillipnicol/OncoBN . -
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