Identifying biological network structure, predicting network behavior, and classifying network state with High Dimensional Model Representation (HDMR).

ABSTRACT: This work presents an adapted Random Sampling - High Dimensional Model Representation (RS-HDMR) algorithm for synergistically addressing three key problems in network biology: (1) identifying the structure of biological networks from multivariate data, (2) predicting network response under previously unsampled conditions, and (3) inferring experimental perturbations based on the observed network state. RS-HDMR is a multivariate regression method that decomposes network interactions into a hierarchy of non-linear component functions. Sensitivity analysis based on these functions provides a clear physical and statistical interpretation of the underlying network structure. The advantages of RS-HDMR include efficient extraction of nonlinear and cooperative network relationships without resorti

SUBMITTER: Miller MA 

PROVIDER: S-EPMC3377689 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature