Using association signal annotations to boost similarity network fusion.

ABSTRACT:

Motivation

Recent technology developments have made it possible to generate various kinds of omics data, which provides opportunities to better solve problems such as disease subtyping or disease mapping using more comprehensive omics data jointly. Among many developed data-integration methods, the similarity network fusion (SNF) method has shown a great potential to identify new disease subtypes through separating similar subjects using multi-omics data. SNF effectively fuses similarity networks with pairwise patient similarity measures from different types of omics data into one fused network using both shared and complementary information across multiple types of omics data.

Results

In this article, we proposed an association-signal-annotation boosted similarity network f

SUBMITTER: Ruan P 

PROVIDER: S-EPMC6761966 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature