A Framework for Discovering Associations from the Annotated Biological Web

During the last decade, biomedical researchers gained access to the entire human genome, reliable high-throughput biotechnologies, and affordable computational resources and network access. In combination, these new tools created a new model for biomedical research that no longer uses computational tools merely to monitor research, but instead exploits these tools to acquire knowledge and make discoveries. Consider a simplified Web of three publicly accessible resources Entrez Gene, OMIM and PubMed, in Figure 1. Data entries in each resource are annotated with terms from multiple controlled vocabularies (CVs). The hyperlinks between data entries in any two resources form a relationship between the two resources and is represented by a (virtual) link. Thus, an entry in Entrez Gene, annotated with GO terms, can have hyperlinks to multiple entries in PubMed that are annotated with MeSH terms.Similarly, OMIM entries, annotated with terms from SNOMED CT may have hyperlinks to entries in Entrez Gene and PubMed. This forms a rich Web of annotated data entries. Our objective in this research is to develop tools to discover meaningful patterns across resources and ontologies. As a first stage in teasing out patterns, we execute a protocol to follow hyperlinks, extract annotations, and generate LSLink datasets. We then mine the term-links of the LSLink datasets to find potentially meaningful associations. Biologically meaningful associations of pairs of CV terms may yield actionable nuggets of previously unknown knowledge. Moreover, the bridge of associations across CV terms will reflect the practice of how scientists annotate data across hyperlinked repositories.