Applying data science methodologies with artificial intelligence variant reinterpretation to map and estimate genetic disorder prevalence utilizing clinical data.

Data science methodologies can be utilized to ascertain and analyze clinical genetic data that is often unstructured and rarely used outside of patient encounters. Genetic variants from all genetic testing resulting to a large pediatric healthcare system for a 5-year period were obtained and reinterpreted utilizing the previously validated Franklin© Artificial Intelligence (AI). Using PowerBI©, the data were further matched to patients in the electronic healthcare record to associate with demographic data to generate a variant data table and mapped by ZIP codes.

A clinicopathologic study of malignancy in VCP-associated multisystem proteinopathy.

Background: Valosin containing protein (VCP) is an important protein with many vital functions mostly related to the ubiquitin-proteasome system that provides protein quality control. VCP-associated inclusion body myopathy with Paget disease of bone and frontotemporal dementia, also termed VCP disease and multisystem proteinopathy (MSP 1), is an autosomal dominant disorder caused by monoallelic variants in the VCP gene on human chromosome 9. VCP has also been strongly involved in cancer, with over-activity of VCP found in several cancers such as prostate, pancreatic, endometrial, esophageal cancers and osteosarcoma.