Description
The vast majority of current knowledge bases contain canonical disease pathways and rarely include uncommon disease mechanisms and drug actions. Furthermore, state-of-the-art knowledge representation (KR) methods still lack a capability to enable uncovering of implicit relationships without applying more complex reasoning techniques. Here, we developed a novel informatics framework for KR and reconciliation of disease pathways for precision medicine analytics. The framework introduces a multi-perspective consensus method for processing concordant, contradictory, and complimentary disease mechanisms.
Learning Objective: After participating in this session, the learner should be better able to:
Understand precision medicine challenges associated with knowledge representation
Undersatnd current challenges in biological knowledge bases' structure and scope
Learn advantages and disadvantages of knowledge representation methods
Learn about data-driven precision medicne analytics
Learn about data-driven medical discoveries
Authors:
Yulia Innokenteva (Presenter)
University of Missouri, Columbia
Olha Kholod, University of Missouri, Columbia
Fei He, Northeast Normal University
Duolin Wang, University of Missouri, Columbia
Richard Hammer, University of Missouri, Columbia
Dong Xu, University of Missouri, Columbia
Dmitriy Shin, University of Missouri, Columbia
Presentation Materials: