SWEET: a single-sample network inference method for deciphering individual features in disease.

ABSTRACT: Recently, extracting inherent biological system information (e.g. cellular networks) from genome-wide expression profiles for developing personalized diagnostic and therapeutic strategies has become increasingly important. However, accurately constructing single-sample networks (SINs) to capture individual characteristics and heterogeneity in disease remains challenging. Here, we propose a sample-specific-weighted correlation network (SWEET) method to model SINs by integrating the genome-wide sample-to-sample correlation (i.e. sample weights) with the differential network between perturbed and aggregate networks. For a group of samples, the genome-wide sample weights can be assessed without prior knowledge of intrinsic subpopulations to address the network edge number bias caused by sample

SUBMITTER: Chen HH 

PROVIDER: S-EPMC10025435 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature