The Application of Knowledge Engineering via the Use of a Biomimetic Digital Twin Ecosystem, Phenotype-Driven Variant Analysis, and Exome Sequencing to Understand the Molecular Mechanisms of Disease

Abstract

Applied Artificial Intelligence, particularly Large Language Models, in biomedical research is accelerating, but effective discovery and validation requires a toolset without limitations or bias. On January 30, 2023, the National Academies of Sciences, Engineering, and Medicine (NAS) appointed an ad hoc committee to identify needs and opportunities to advance the mathematical, statistical, and computational foundations of digital twins in applications across science, medicine, engineering, and society. On December 15, 2023, the NAS released a 164 page report, "Foundational Research Gaps and Future Directions for Digital Twins". This report described the importance of using digital twins in biomedical research. We developed an innovative method that incorporated phenotype ranking algorithms with knowledge engineering via a biomimetic digital twin ecosystem. This ecosystem applied real-world reasoning principles to non-normalized, raw data to identify hidden or "dark data". We performed a clinical exome sequencing study on patients with endometriosis and were able to identify four VUSs potentially associated with endometriosis-related disorders in nearly all patients analyzed. One VUS was identified in all patient samples and could be a biomarker for diagnostics. To the best of our knowledge, this is the first study to incorporate the recomandations of the NAS to biomedical research. This method can be used to understand the mechanisms of any disease, for virtual clinical trials, and to identify effective new therapies.