Data Science & Analytics
ROBOKOP U24
Description
The volume of biomedical research data stored in various databases has grown immensely in recent years due to the proliferation of high-throughput biomedical ‘-omics’ technologies. Nearly all of respective databases, or ‘knowledge sources’ (KSs), address a particular area of biomedical research, leading to natural diversity but also growing disintegration between individual KSs, which generates downstream inefficiencies when mining diverse databases for knowledge discovery. Austin and colleagues described this situation as the ‘Translational Tower of Babel’ [1]. Yet, in the age of team science and cross-disciplinary research, translational research questions often require co-exploration of multiple FAIR (Findable, Accessible, Interoperable, Reusable)-compliant [2] KSs that must be connected and integrated to deconstruct the tower and identify answers and solutions.
Expanding efforts, both in academia and industry, are focused on the development of methods and tools to enable semantic integration and concurrent exploration of disparate biomedical KSs, using specially constructed biomedical ‘graph knowledgebases’ (GKBs) that support the generation of new knowledge through the application of reasoning tools and algorithms. Our team has contributed to these efforts by initiating the development of a GKB-based question-answering system termed Reasoning Over Biomedical Objects linked in Knowledge-Oriented Pathways (ROBOKOP) [3], [4]. ROBOKOP’s publicly accessible user interface (UI) [5] allows users to address both relatively simple questions such as “what genes are associated with drug-induced liver injury?” and more complex ones such as “what drugs could be used to treat airborne pollutant–induced asthma exacerbations in patients who are non-responsive to traditional medications?”
RENCI's Role
Alex Tropsha and Chris Bizon are the PIs for this project.
