Databases of biomedical knowledge are rapidly proliferating and growing, with recent advances (such as the RTX-KG2 knowledge-base that we have recently developed; (Wood et al. 2022)) increasingly focusing on integration of knowledge under a standardized schema and semantic layer (i.e., controlled vocabularies for types of concepts and types of relationships, for example, the Biolink standard (Unni et al. 2022)). The rise of standardized knowledge-bases sets the stage for the development of computational systems that can systematically discover novel connections between drugs and diseases (i.e., large-scale computational drug repurposing) or to answer other kinds of translational questions (e.g., "What anticonvulsants are likely to have drug-drug interactions with cannabinoids?" (Vázquez et al. 2020) or "What drugs would downregulate expression of RHOBTB2 in the central nervous system?" (Foksinska et al. 2022)). To be able to build such a system, improved methods and representation languages for knowledge-graph-based computational reasoning are needed. Previous efforts contributed myriad tools and approaches, but progress for biomedical reasoning systems has been hindered by (1) the lack of an expressive analysis workflow language for translational reasoning and (2) the lack of an associated reasoning engine that federates semantically integrated knowledge-bases. As a part of the NCATS Translator project (Biomedical Data Translator Consortium 2019), we have developed ARAX (Glen et al. 2023), which is a new computational reasoning system for translational biomedicine that combines (1) an innovative workflow language (ARAXi), (2) a comprehensive semantically-unified biomedical knowledge graph (RTX-KG2), and (3) a versatile and novel method for scoring search results. Users or application-builders can query ARAX via a web browser interface or a web application programming interface. ARAX enables users to encode translational biomedical questions and to integrate knowledge across sources to answer the user’s query and facilitate exploration of results. To illustrate ARAX’s application and utility in specific disease contexts, we will present and discuss several use-case examples.
- Source code and technical documentation: github:RTXteam/RTX
- Hosted ARAX service with a web browser interface: arax.rtx.ai
- Web application programming interface (API): arax.rtx.ai/api/arax/v1.3/ui/
The principal investigators for this project are
- David Koslicki (Penn State University)
- Eric Deutsch (Institute for Systems Biology)
- Stephen Ramsey (Oregon State University)
- Biomedical Data Translator Consortium. 2019. “Toward A Universal Biomedical Data Translator.” Clinical and Translational Science 12 (2): 86–90.
- Foksinska, Aleksandra, Camerron M. Crowder, Andrew B. Crouse, Jeff Henrikson, William E. Byrd, Gregory Rosenblatt, Michael J. Patton, et al. 2022. “The Precision Medicine Process for Treating Rare Disease Using the Artificial Intelligence Tool miniKanren.” Frontiers in Artificial Intelligence 5 (September): 910216.
- Glen, Amy K., Chunyu Ma, Luis Mendoza, Finn Womack, E. C. Wood, Meghamala Sinha, Liliana Acevedo, et al. 2023. “ARAX: A Graph-Based Modular Reasoning Tool for Translational Biomedicine.” bioRxiv. https://doi.org/10.1101/2022.08.12.503810.
- Unni, Deepak R., Sierra A. T. Moxon, Michael Bada, Matthew Brush, Richard Bruskiewich, J. Harry Caufield, Paul A. Clemons, et al. 2022. “Biolink Model: A Universal Schema for Knowledge Graphs in Clinical, Biomedical, and Translational Science.” Clinical and Translational Science 15 (8): 1848–55.
- Vázquez, Marta, Natalia Guevara, Cecilia Maldonado, Paulo Cáceres Guido, and Paula Schaiquevich. 2020. “Potential Pharmacokinetic Drug-Drug Interactions between Cannabinoids and Drugs Used for Chronic Pain.” BioMed Research International 2020 (August): 3902740.
- Wood, E. C., Amy K. Glen, Lindsey G. Kvarfordt, Finn Womack, Liliana Acevedo, Timothy S. Yoon, Chunyu Ma, et al. 2022. “RTX-KG2: A System for Building a Semantically Standardized Knowledge Graph for Translational Biomedicine.” BMC Bioinformatics 23 (400). https://doi.org/10.1186/s12859‐022‐04932‐3.
Programme: Independent Projects
SEEK ID: https://fairdomhub.org/projects/347Funding codes:
- NIH OT2TR003428
Public web page: https://github.com/RTXteam/RTX
Organisms: Homo sapiens
FAIRDOM PALs: No PALs for this Project
Project start date: 1st Jan 2020
Project end date: 28th Nov 2023