Predicting viral exposure response from modeling the changes of co-expression networks using time series gene expression data.

ABSTRACT:

Background

Deciphering the relationship between clinical responses and gene expression profiles may shed light on the mechanisms underlying diseases. Most existing literature has focused on exploring such relationship from cross-sectional gene expression data. It is likely that the dynamic nature of time-series gene expression data is more informative in predicting clinical response and revealing the physiological process of disease development. However, it remains challenging to extract useful dynamic information from time-series gene expression data.

Results

We propose a statistical framework built on considering co-expression network changes across time from time series gene expression data. It first detects change point for co-expression networks and then employs a Bayes

SUBMITTER: Dong F 

PROVIDER: S-EPMC7449007 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature