Building a Knowledge Graph Representing Causal Associations Between Risk Factors and Incidence of Breast Cancer

This paper explores the use of semantic- and evidence-based biomedical knowledge to build RiskExplorer graph that outlines causal associations between risk factors chronic disease or cancers. The intent this work is offer an interactive synthesis platform empower health-information-seeking individuals learn about mitigate modifiable factors. Our approach analyzes text (from PubMed abstracts), Semantic Medline database, semantic associations, literature-based discovery, database discover breast cancer. methodological framework involves (a) identifying relevant literature on specified diseases cancers, (b) extracting via mining tool, (c) building rich by transforming nodes edges, (d) applying frequency-based methods using edge properties traverse identify meaningful multi-node NCD paths. Generated paths consist a source node (representing factor), one more intermediate phenotypes), target cancer), edges representing associations. results demonstrate our methodology capable generating biomedically valid related protective