A hierarchical Bayesian model for flexible module discovery in three-way time-series data

摘要

Motivation: Detecting modules of co-ordinated activity is fundamental in the analysis of large biological studies. For two-dimensional data (e.g. genes x patients), this is often done via clustering or biclustering. More recently, studies monitoring patients over time have added another dimension. Analysis is much more challenging in this case, especially when time measurements are not synchronized. New methods that can analyze three-way data are thus needed.