Toward Genomics Knowledge Representation for Clinical Trials

Workshop at 2006 AMIA Symposium



The ability to measure patient information at the genomic level and its utility for stratifying clinically responsive groups is driving the next generation of drug therapies. As a result, clinical trials are increasingly incorporating molecular biology-based assays yielding genomic and proteomic data. Effective management and analysis of this data requires knowledge representation systems that span the biological and medical domains. For this purpose, AMIA’s Genomics Working Group (GEN-WG), Knowledge Representation Working Group (KR-WG), and Clinical Trials Working Group (CT-WG) have collaborated to develop a workshop in an effort to exploit the overlap between the three domains.


Workshop Description

We will begin with presentations on biomedical ontologies and on the role they can play in providing representation frameworks which can comprehend entities both at the genomics and at the clinical levels of granularity. The key issues to be discussed will be:

        Differentiation between continuant and occurrent entities

        Proper treatment of fundamental relations in genomics

        The ontology of clinical trials


We will next discuss several biological ontologies which should be used in conjunction with a clinical trial ontology to adequately represent and analyze clinical trial data. We will focus on the ontological representations of cells and proteins, and then explore the clinical application of the resulting ontologies. Using as an example a large case-control study of progression to sepsis after Staph aureus infection, we will describe the role of the cell and protein ontologies in interpreting the correlation between genomic and phenotypic patient information and in identifying host susceptility genes.


We will then discuss how systems for knowledge representation can in principle support reasoning about alternative hypotheses, using as our test case the HyBrow system. The HyBrow (Hypothesis Browser) system is a prototype tool for designing hypotheses and evaluating them for consistency with existing knowledge. HyBrow consists of a contradiction-based reasoning framework with the ability to reason with diverse biological information, an ontology for composing hypotheses about biological systems at different levels of granularity, a knowledgebase to structure information according to the ontology, and programs to design, evaluate and revise hypotheses. We will discuss how such systems can be used to assist researchers in making sense of large amounts of omic information.


We will then describe the Immune Tolerance Network, a collaborative research effort that solicits, develops, implements and evaluates clinical trials and related biological assays for the purposes of inducing, maintaining and monitoring tolerance in humans for kidney, liver and islet transplantation, autoimmune diseases and allergy and asthma. The approach integrates phenotypic, molecular and clinical data represented in context of the events and timings of the specific study and based on the control ontology.


Finally, we will present some applications of Grid technology for life sciences data integration being used to integrate clinical genomics data to be used for clinical trials especially within the framework of the EU-funded ACGT project on Advancing Clinico-Genomics Trials.


Educational Goals:

        Role of knowledge representation and formal ontologies in the field of clinical genomics.

        Role of genomics data management in clinical trials.

        Ontologies related to clinical trials.


Who should attend:

AMIA attendees interested in exploiting the overlap between the domains of Genomics, Knowledge Representation and Clinical Trials.



        Barry Smith (University at Buffalo, NY, USA and Institute for Formal Ontology and Medical Information Science, Saarbrücken, Germany)

        Lindsay Cowell (Duke University, NC, USA)

        Nigam Shah (Stanford University, CA, USA)

        Dave Parrish (Immune Tolerance Network, PA, USA)

        Yannick Legre (Healthgrid, France)



        Werner Ceusters (University at Buffalo, NY, USA ) and Shawn Levy (Vanderbilt University, TN, USA)



        Anand Kumar (Institute for Formal Ontology and Medical Information Science, Saarbrücken, Germany)