An approach for collaborative development of a federated biomedical knowledge graph-based question-answering system: Question-of-the-Month challenges.

Knowledge graphs have become a common approach for knowledge representation. Yet, the application of graph methodology is elusive due to the sheer number and complexity of knowledge sources. In addition, semantic incompatibilities hinder efforts to harmonize and integrate across these diverse sources. As part of The Biomedical Translator Consortium, we have developed a knowledge graph-based question-answering system designed to augment human reasoning and accelerate translational scientific discovery: the Translator system. We have applied the Translator system to answer biomedical questions in the context of a broad array of diseases and syndromes, including Fanconi anemia, primary ciliary dyskinesia, multiple sclerosis, and others. A variety of collaborative approaches have been used to research and develop the Translator system. One recent approach involved the establishment of a monthly "Question-of-the-Month (QotM) Challenge" series. Herein, we describe the structure of the QotM Challenge; the six challenges that have been conducted to date on drug-induced liver injury, cannabidiol toxicity, coronavirus infection, diabetes, psoriatic arthritis, and ATP1A3-related phenotypes; the scientific insights that have been gleaned during the challenges; and the technical issues that were identified over the course of the challenges and that can now be addressed to foster further development of the prototype Translator system. We close with a discussion on Large Language Models such as ChatGPT and highlight differences between those models and the Translator system.

The precision medicine process for treating rare disease using the artificial intelligence tool mediKanren.

There are over 6,000 different rare diseases estimated to impact 300 million people worldwide. As genetic testing becomes more common practice in the clinical setting, the number of rare disease diagnoses will continue to increase, resulting in the need for novel treatment options. Identifying treatments for these disorders is challenging due to a limited understanding of disease mechanisms, small cohort sizes, interindividual symptom variability, and little commercial incentive to develop new treatments. A promising avenue for treatment is drug repurposing, where FDA-approved drugs are repositioned as novel treatments. However, linking disease mechanisms to drug action can be extraordinarily difficult and requires a depth of knowledge across multiple fields, which is complicated by the rapid pace of biomedical knowledge discovery. To address these challenges, The Hugh Kaul Precision Medicine Institute developed an artificial intelligence tool, mediKanren, that leverages the mechanistic insight of genetic disorders to identify therapeutic options. Using knowledge graphs, mediKanren enables an efficient way to link all relevant literature and databases. This tool has allowed for a scalable process that has been used to help over 500 rare disease families. Here, we provide a description of our process, the advantages of mediKanren, and its impact on rare disease patients.

Application of MCAT questions as a testing tool and evaluation metric for knowledge graph-based reasoning systems.

"Knowledge graphs" (KGs) have become a common approach for representing biomedical knowledge. In a KG, multiple biomedical data sets can be linked together as a graph representation, with nodes representing entities, such as "chemical substance" or "genes," and edges representing predicates, such as "causes" or "treats." Reasoning and inference algorithms can then be applied to the KG and used to generate new knowledge. We developed three KG-based question-answering systems as part of the Biomedical Data Translator program. These systems are typically tested and evaluated using traditional software engineering tools and approaches. In this study, we explored a team-based approach to test and evaluate the prototype "Translator Reasoners" through the application of Medical College Admission Test (MCAT) questions. Specifically, we describe three "hackathons," in which the developers of each of the three systems worked together with a moderator to determine whether the applications could be used to solve MCAT questions. The results demonstrate progressive improvement in system performance, with 0% (0/5) correct answers during the first hackathon, 75% (3/4) correct during the second hackathon, and 100% (5/5) correct during the final hackathon. We discuss the technical and sociologic lessons learned and conclude that MCAT questions can be applied successfully in the context of moderated hackathons to test and evaluate prototype KG-based question-answering systems, identify gaps in current capabilities, and improve performance. Finally, we highlight several published clinical and translational science applications of the Translator Reasoners.

Development of IsoTaG, a Chemical Glycoproteomics Technique for Profiling Intact N- and O-Glycopeptides from Whole Cell Proteomes.

Protein glycosylation can have an enormous variety of biological consequences, reflecting the molecular diversity encoded in glycan structures. This same structural diversity has imposed major challenges on the development of methods to study the intact glycoproteome. We recently introduced a method termed isotope-targeted glycoproteomics (IsoTaG), which utilizes isotope recoding to characterize azidosugar-labeled glycopeptides bearing fully intact glycans. Here, we describe the broad application of the method to analyze glycoproteomes from a collection of tissue-diverse cell lines. The effort was enabled by a new high-fidelity pattern-searching and glycopeptide validation algorithm termed IsoStamp v2.0, as well as by novel stable isotope probes. Application of the IsoTaG platform to 15 cell lines metabolically labeled with Ac4GalNAz or Ac4ManNAz revealed 1375 N- and 2159 O-glycopeptides, variously modified with 74 discrete glycan structures. Glycopeptide-bound glycans observed by IsoTaG were found to be comparable to released N-glycans identified by permethylation analysis. IsoTaG is therefore positioned to enhance structural understanding of the glycoproteome.