Modelling cancer progression using Mutual Hazard Networks.

ABSTRACT: MOTIVATION:Cancer progresses by accumulating genomic events, such as mutations and copy number alterations, whose chronological order is key to understanding the disease but difficult to observe. Instead, cancer progression models use co-occurrence patterns in cross-sectional data to infer epistatic interactions between events and thereby uncover their most likely order of occurrence. State-of-the-art progression models, however, are limited by mathematical tractability and only allow events to interact in directed acyclic graphs, to promote but not inhibit subsequent events, or to be mutually exclusive in distinct groups that cannot overlap. RESULTS:Here we propose Mutual Hazard Networks (MHN), a new Machine Learning algorithm to infer cyclic progression models from cross-sectional data.

SUBMITTER: Schill R 

PROVIDER: S-EPMC6956791 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature