Paper on the groundbreaking research by Genzeva, LumaGene, RYLTI, Brigham & Women’s Hospital of Harvard University, and QIAGEN Digital Insights was published this week in the Journal of Molecular Diagnostics (https://www.jmdjournal.org/article/S1525-1578(24)00062-X/fulltext)
ROCKVILLE, MD, April 03, 2024 / Biotech Newswire / -- Revolutionary genomic research by Genzeva, LumaGene, RYLTI, Brigham & Women’s Hospital of Harvard University, and QIAGEN Digital Insights applied an innovative use of multiomics and biomimetic digital twins to unveil new DNA variants associated with the development of endometrial related disorders. This study marks a significant leap forward in understanding the molecular mechanisms of endometrial-related disorders.
In a paper published this week in the Journal of Molecular Diagnostics, the study led by geneticist Dr. William G. Kearns, Co-founder, CEO and Chief Scientific Officer of Genzeva and LumaGene, employed cutting-edge methodologies incorporating clinical exome sequencing, phenotype-driven variant analysis, and RYLTI's Knowledge Engineering (RKE) Biomimetic AI Platform and Digital Twin Ecosystem, to analyze patient samples with endometriosis. This unprecedented multidimensional approach uncovered novel insights by analyzing hidden “dark” data, which allowed the team to identify eight pathogenic mutations and four variants of unknown clinical significance (VUSs) relevant to this disorder.
In addition to identifying pathogenic DNA variants the research also identified four VUSs potentially associated with the development of endometrial-related disorders. One VUS identified in the MUC20 gene was present across all patient samples and could be a potential biomarker for diagnostics. The other identified VUSs in genes USP17L1, FAM66B, and DEFB109B all mapped to the short arm of chromosome 8. The expression of the DEFB109B and FAM66B genes are regulated by the same gene enhancer.
The paper, “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,” was authored by Kearns and collaborators Dr. J. Georgios Stamoulis (QIAGEN Digital Insights), Joseph Glick and Lawrence Baisch (RYLTI BioPharma); Andrew Benner, Dalton Brough, Dr. Luke Du (Genzeva); Dr. Bradford Wilson (IndyGeneUS AI); Laura Kearns (Genzeva and LumaGene); Nicholas Ng, Maya Seshan, and Dr. Raymond Anchan (Brigham and Women’s Hospital, Harvard University).
“This landmark study demonstrated a way to combine omics and a digital twin ecosystem to understand the molecular mechanism of disease better. By incorporating Genzeva’s multiomics platform, QIAGEN’s Digital Insight, and RYLTI’s pioneering biomimetic AI platform for genomic analyses we uncovered hidden dark data with insights that may never have been achievable before. The possibility to redefine this scientific approach promises a new horizon in many fields of research, and the biotech industry eagerly anticipates the impact of these innovative techniques on future discoveries and therapeutic interventions,” commented Dr. Kearns.
In January, Life Sciences Review Magazine awarded RYLTI Top Biomimetic AI Platform of 2024 for its biomimetic AI digital twin ecosystem innovation, hailing the technology for opening new doors to discovery and accelerating the innovation process.
Dr. Ray Anchan, Asst. Professor Harvard Medical School/Co-Director, Brigham, and Women’s Hospital Center for Endometriosis, said: "The computational capacity of knowledge engineering using a biomimetic digital twin ecosystem provides a unique opportunity to efficiently identify novel and unique DNA variants in various disease processes that could prove useful for developing new targeted therapies.”
"The NAS has described the importance of using digital twins in biomedical research, which requires a toolset without limitations or bias for effective discovery and validation," said Joseph Glick, an award-winning biomimetic AI pioneer and Co-founder/Chief Innovation Officer of RYLTI. “This new innovative omics method incorporating phenotype ranking algorithms with digital twins demonstrates the potential to enhance the drug discovery process with the inclusion of data, enabling improved decision making, expedited development timelines, refined target identification, and reduced risks.”
“This exciting breakthrough with use of the first biomimetic digital twins comes at a very exciting time for us, not only in advanced genomic research, but across biopharma and in other sectors,” said Peter Fiorillo, Co-founder and CEO of RYLTI.
About GENZEVA
GENZEVA is a leading molecular diagnostic laboratory utilizing advanced multiomicsand patent-pending technology solutions. It provides services to healthcare research organizations worldwide, clinicians, and referring laboratories.
About RYLTI BioPharma
RYLTI is the architect behind its revolutionary RYAILITI Knowledge Engineering (RKE) Biomimetic AI Digital Twin Platform and innovative solutions for a variety of industries, including the pharmaceutical, biotechnology, renewable energy, telecommunications, financial services, and technology industries, among other sectors.
Contacts
Peter Fiorillo
CEO RYLTI BioPharma Inc
This email address is being protected from spambots. You need JavaScript enabled to view it.
+1.631.806.2108
Dr. William G. Kearns
CEO and CSO Genzeva and LumaGene
This email address is being protected from spambots. You need JavaScript enabled to view it.
+1.301.366.1666
Keywords: Female; Endometriosis; Biomimetics; Chromosomes, Human, Pair 8; Exome Sequencing; Multiomics; Phenotype; Biomarkers; Hospitals; DNA; Pathology, Molecular; United States; Genzeva; LumaGene; RYLTI; Brigham & Women’s Hospital of Harvard University; QIAGEN Digital Insights; Biomimetic AI; Digital twins; Genetics research; Journal of Molecular Diagnostics; Endometrial-related disorders; DNA variants; Clinical exome sequencing; Phenotype-driven variant analysis; Knowledge Engineering (RKE) Biomimetic AI Platform; Patient samples; Pathogenic mutations; Variants of unknown clinical significance (VUSs); MUC20 gene; USP17L1 gene; FAM66B gene; DEFB109B gene; J. Georgios Stamoulis; Joseph Glick; Lawrence Baisch; Andrew Benner; Dalton Brough; Luke Du; Bradford Wilson; Laura Kearns; Nicholas Ng; Maya Seshan; Raymond Anchan; Biomimetic AI Platform; Computational capacity; Therapeutic interventions; Endometriosis; Harvard Medical School; NAS (National Academy of Sciences); Drug discovery process; Phenotype ranking algorithms; Peter Fiorillo; Molecular diagnostic laboratory; Patent-pending technology solutions; Healthcare research organizations; RYAILITI Knowledge Engineering (RKE) Biomimetic AI Digital Twin Platform
Published by Biotech Newswire