Unified feature association networks through integration of transcriptomic and proteomic data.

ABSTRACT: High-throughput multi-omics studies and corresponding network analyses of multi-omic data have rapidly expanded their impact over the last 10 years. As biological features of different types (e.g. transcripts, proteins, metabolites) interact within cellular systems, the greatest amount of knowledge can be gained from networks that incorporate multiple types of -omic data. However, biological and technical sources of variation diminish the ability to detect cross-type associations, yielding networks dominated by communities comprised of nodes of the same type. We describe here network building methods that can maximize edges between nodes of different data types leading to integrated networks, networks that have a large number of edges that link nodes of different-omic types (transcripts, p

SUBMITTER: McClure RS 

PROVIDER: S-EPMC6748406 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature