Inferring tumor progression in large datasets.

ABSTRACT: Identification of mutations of the genes that give cancer a selective advantage is an important step towards research and clinical objectives. As such, there has been a growing interest in developing methods for identification of driver genes and their temporal order within a single patient (intra-tumor) as well as across a cohort of patients (inter-tumor). In this paper, we develop a probabilistic model for tumor progression, in which the driver genes are clustered into several ordered driver pathways. We develop an efficient inference algorithm that exhibits favorable scalability to the number of genes and samples compared to a previously introduced ILP-based method. Adopting a probabilistic approach also allows principled approaches to model selection and uncertainty quantification. Usi

SUBMITTER: Mohaghegh Neyshabouri M 

PROVIDER: S-EPMC7577444 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature