HyperHMM: efficient inference of evolutionary and progressive dynamics on hypercubic transition graphs.

ABSTRACT:

Motivation

The evolution of bacterial drug resistance and other features in biology, the progression of cancer and other diseases and a wide range of broader questions can often be viewed as the sequential stochastic acquisition of binary traits (e.g. genetic changes, symptoms or characters). Using potentially noisy or incomplete data to learn the sequences by which such traits are acquired is a problem of general interest. The problem is complicated for large numbers of traits, which may, individually or synergistically, influence the probability of further acquisitions both positively and negatively. Hypercubic inference approaches, based on hidden Markov models on a hypercubic transition network, address these complications, but previous Bayesian instances can consume substantia

SUBMITTER: Moen MT 

PROVIDER: S-EPMC9848056 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature