EVIDARA: Automated Evidential Support from Raw Data for relay agents in Biomedical KG Queries

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1) Component: Autonomous Relay Agent. We will develop an ARA named EVIDARA to evaluate returns from queries in knowledge sources (KS) using a new epistemology: The “reasoning” is based on checking against empirical evidence available in raw data (measurements) instead of deductive reasoning (FIG.►). EVIDARA will assist the Autonomous Relay System (ARS) to identify paths in returned knowledge graphs (KG) that may conflict with real-word evidence and to relay queries to appropriate specialty KS or database. (2) Problem addressed: EHR and multi-omics raw data from large cohorts, if properly preprocessed [e.g., by Knowledge Providers, such as the DOCKET, see application by Dr. Glusman], offers a new opportunity for ad hoc systematic extraction of empirical knowledge on relationships (“Protein P level correlates with risk for disease D”) instead of relying on specific epidemiological analyses. The problem in harnessing raw data for empirical support in lieu of deductive reasoning is that the KGs to be evaluated are extracted from knowledge sources of distinct types and that the relevance of paths depends on the query context Q. Also the ARA algorithm should be scalable to digest the emerging multi-omics data from projects like All-of-Us, the UK Biobank. (3) Plan for implementation: Research will be conducted to evaluate a new epistemic realm: make empirical evidence central to “reasoning”. We have assembled a set of functioning tools to overcome the chicken-egg problem of getting a project started and jumpstart development and testing of EVIDARA: (i) SPOKE, one of the largest biomedical knowledge network (KN) has integrated 25 diverse of KS into a single (neo4j) network database of 2 million nodes and will serve as testing ground for research well before we can use KGs produced by the Knowledge Providers. (ii) Algorithms that use raw data from EHR and multi-omics studies to evaluate the returned KGs. For instance, we compute weights of all nodes in the entire KN through a random-walk algorithm biased by their role for a given condition Q observed in the raw data. (iii) Raw data beyond EHR: multi-omics profiles from a study at ISB with >10k variables which vastly exceeds coverage of observable nodes in KNs offered by EHRs. Example query: “Vitamin K stimulates stem-cell signaling, thus could promote cancer. What is the molecular pathway? Mechanisms returned as KG will be pruned by EVIDARA and checked against correlative evidence in the raw data: Is there evidence that taking Vit. K or its antagonist reduces cancer risk?”. Importantly, since EVIDARA learns on a network of many types of KS, it will provide information to the ARS about which type of KS/Knowledge Provider to invoke next (in iterative queries) to improve the knowledge graph. (4) Expertise & resources: The MPIs, Drs. S. Baranzini (UCSF) and S. Huang (ISB) are researchers with long history of working with medical big data, thus offering technical expertise and the critical SME perspective. SB’s team has created and maintains SPOKE. The uniquely self-contained SPOKE network will allow NCATS staff to test other ARAs. SH brings decades of experience in research of disease mechanisms and medical epistemology. His team will provide multi-omics datasets and data analytics expertise. With his prior work in the NCATS Translator program, he is well poised to maximize team science efficiency and help convert its vision into tangible results. (5) Potential challenges. (i) Quality of evidential support depends on quality of raw data. A quality control is beyond the scope of EVIDARA but could be provided by Knowledge Providers focusing on new multi-omics data sets (e.g. DOCKET). (ii) Testing EVIDARA on other KS from Knowledge Providers) may be slowed down by interoperability issues (e.g. incompatible identifiers). Such issues will be addressed early in Year 1 with help of the Standard and Reference group.