Clustering approaches for visual knowledge exploration in molecular interaction networks.

ABSTRACT:

Background

Biomedical knowledge grows in complexity, and becomes encoded in network-based repositories, which include focused, expert-drawn diagrams, networks of evidence-based associations and established ontologies. Combining these structured information sources is an important computational challenge, as large graphs are difficult to analyze visually.

Results

We investigate knowledge discovery in manually curated and annotated molecular interaction diagrams. To evaluate similarity of content we use: i) Euclidean distance in expert-drawn diagrams, ii) shortest path distance using the underlying network and iii) ontology-based distance. We employ clustering with these metrics used separately and in pairwise combinations. We propose a novel bi-level optimization approach tog

SUBMITTER: Ostaszewski M 

PROVIDER: S-EPMC6116538 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature