Announcing the Biomedical Data Translator: Initial Public Release

Abstract

The growing availability of biomedical data offers vast potential to improve human health, but the complexity and lack of integration of these datasets often limit their utility. To address this, the Biomedical Data Translator Consortium has developed an open-source knowledge graph-based system-Translator-designed to integrate, harmonize, and make inferences over diverse biomedical data sources. We announce here Translator's initial public release and provide an overview of its architecture, standards, user interface, and core features. Translator employs a scalable, federated, knowledge graph framework for the integration of clinical, genomic, pharmacological, and other biomedical knowledge sources, enabling query retrieval, inference, and hypothesis generation. Translator's user interface is designed to support the exploration of knowledge relationships and the generation of insights, without requiring deep technical expertise and gradually revealing more detailed evidence, provenance, and confidence information, as needed by a given user. To demonstrate Translator's application and impact, we highlight features of the user interface in the context of three real-world use cases: suggesting potential therapeutics for patients with rare disease; explaining the mechanism of action of a pipeline drug; and screening and validating drug candidates in a model organism. We discuss strengths and limitations of reasoning within a largely federated system and the need for rich concept modeling and deep provenance tracking. Finally, we outline future directions for enhancing Translator's functionality and expanding its data sources. Translator represents a significant step forward in making complex biomedical knowledge more accessible and actionable, aiming to accelerate translational research and improve patient care.

FIGURE 1

Translator UI and select features. (A) Users select a templated question or search option as an initial step when using Translator. (B) After selecting a template, users then select a search term, “Adrenocortical insufficiency” in this example, facilitated by the autocomplete feature. (C) After running a query, users may filter results by a number of facets, including “chemical category” and “chemical role classification”. (D) Translator evidence and provenance are returned by the UI to support each answer. In this example, the user asked: “what drugs may treat conditions related to adrenocortical insufficiency?” After filtering for “drug” and excluding “adjuvant”, “aetiopathogenetic role”, and “diagnostic agent”, Translator returned “hydrocortisone cypionate” as the top result. Three direct paths were returned. One direct path was supported by two different primary knowledge sources, DrugCentral and SIDER, with URLs to Translator Wiki pages that include a brief description of the source and example edges or records. The indirect evidence included a path that asserted “hydrocortisone cypionate is in clinical trials for chronic primary adrenal insufficiency, which is a subclass of adrenocortical insufficiency,” with URLs provided for the relevant clinical trials. Results can be found at: https://ui.transltr.io/results?l=Adrenocortical%20Insufficiency&i=MONDO:0000004&t=0&r=0&q=453bdb5c‐e076‐4471‐ad17‐90185d364b18 (generated 02:44 PM EST, October 28, 2024).

FIGURE 2

Use case: Suggesting potential therapeutics for a patient with rare disease. (A) Conceptual framework. This use case was driven by a patient with a loss‐of‐function genetic variant in the MET gene that presented clinically as non‐alcoholic fatty liver disease (NAFLD). Translator was used to seek chemical entities that cause an increase in the activity or abundance of MET but are not associated with cancer, a common occurrence with overexpression of MET. (B) After filtering by “drug” and chemical role of “pathway inhibitor”, Translator returned etoposide as the top answer, along with a brief description of the drug and a flag noting that it has an additional chemical role of “antineoplastic agent”. The indirect assertion was accompanied by nine supporting paths. For example, one path asserted that “etoposide causes increased activity or abundance of AKT1, which upregulates MET”. Results can be found at: https://ui.transltr.io/results?l=MET%20(Human)&i=NCBIGene:4233&t=1&r=0&q=1e513a61‐9d3a‐4aee‐90c4‐8e42c004933f (generated 2:32 PM EST, October 25, 2024).

FIGURE 3

Use case: Exploring and explaining the mechanism of action of a drug. (A) Conceptual framework. This use case focused on celiprolol, a pipeline drug in clinical trials for the treatment of vascular Ehlers Danlos Syndrome (vEDS). The lack of an acceptable animal model of vEDS prompted the use of Translator to suggest a mechanism of action to support celiprolol's efficacy in the treatment of vEDS. Translator sought both direct and indirect evidence for celiprolol decreasing the activity of an unspecified Gene B. (B) Among the direct evidence was an assertion that “ADRB1 has decreased activity caused by celiprolol,” with two direct edges, one of which was from DrugBank and included a supporting publication. (C) Among the indirect evidence was an assertion that “COL1A1 is downregulated by ADRB2, which has increased activity or abundance caused by celiprolol”. Results can be found at: https://ui.transltr.io/results?l=Celiprolol&i=CHEBI:94461&t=4&r=0&q=fd0628dd‐54c5‐490d‐98b6‐89483d35ad00 (generated 03:47 PM EST, October 18, 2024).

FIGURE 4

Use case: Screening and validating drug candidates using a model organism. (A) Conceptual framework. This use case focused on a rare syndrome associated with seizures and other neurological symptoms and caused by bi‐allelic, loss‐of‐function variants in the CAMSAP1 gene. Translator was used to propose drug candidates targeting molecular signaling pathways known to involve CAMSAPs for subsequent in vitro screening in a C. elegans model of the disease. (B) One of the drugs identified as a target for KIF2A was acetylsalicylic acid. An indirect supporting path asserted that “acetylsalicylic acid causes increased activity or abundance of BDNF, which causes downregulated activity or abundance of KIF2A”. Results can be found at: https://ui.transltr.io/results?l=KIF2A%20(Human)&i=NCBIGene:3796&t=2&r=23d52e95&q=b3562865‐bda6‐4e8c‐a83f‐065004983843 (generated 03:03 PM CST, November 06, 2024).