HyperTraPS-CT: Inference and prediction for accumulation pathways with flexible data and model structures.

ABSTRACT: Accumulation processes, where many potentially coupled features are acquired over time, occur throughout the sciences from evolutionary biology to disease progression, and particularly in the study of cancer progression. Existing methods for learning the dynamics of such systems typically assume limited (often pairwise) relationships between feature subsets, cross-sectional or untimed observations, small feature sets, or discrete orderings of events. Here we introduce HyperTraPS-CT (Hypercubic Transition Path Sampling in Continuous Time) to compute posterior distributions on continuous-time dynamics of many, arbitrarily coupled, traits in unrestricted state spaces, accounting for uncertainty in observations and their timings. We demonstrate the capacity of HyperTraPS-CT to deal with cross-

SUBMITTER: Aga ONL 

PROVIDER: S-EPMC11404842 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature