Milestone 2:<\/b> Develop EpiPred, a novel epilepsy-specific computational predictive model, using EpiMVP Project-generated and existing functional data to accurately predict the likelihood of a variant being associated with epilepsy.<\/b><\/h3>\n
We\u2019ll use a variety of resources to characterize variants, including:\u00a0<\/span><\/p>\n\n- Existing variant interpretation annotation tools\u00a0<\/span><\/li>\n
- Proteomics and structural modeling\u00a0<\/span><\/li>\n
- EpiMVP functional data<\/span><\/li>\n<\/ul>\n
EpiPred for variant classification and prioritization<\/b> will be devised following an iterative machine learning model. Annotated variant data will be used as input to EpiPred for prioritization of missense variants predicted to be pathogenic, VUS or benign. This will aid functional characterization in projects 1-3, and functional data will be used as input in the model. In this iterative process, increasing levels of complex functional data will be used to hone the accuracy of EpiPred to classify variants.<\/span><\/p>\nMilestone 3:<\/b> To support data management and web-based resources needed for seamless data sharing and implementation of EpiPred in the epilepsy community.<\/b><\/h3>\n\n- The GVCC will co-ordinate data from all participating insititutaions.\u00a0\u00a0\u00a0<\/span><\/li>\n
- EpiPred will eventually have a web-facing version that will allow users (clinical care providers and patients\/families) to generate prediction on their own variants.\u00a0<\/span><\/li>\n
- We will also work with ClinGen, ClinVar, the ACMG and industry partners to integrate the prediction score into standard genetic testing results.<\/span><\/li>\n<\/ul>\n<\/div>\n<\/div>\n\n\n
\n
EpiPred classification and integration<\/p><\/div>\n<\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"
The Gene and Variant Curation Core (GVCC) is to be the central hub for decision making of the genes and variants for study by the Epilepsy Multiplatform Variant Prediction (EpiMVP) projects in this Center Without Walls (CWOW). The core will integrate genetic sequence data from population and patient cohorts, as well as the functional readouts from the EpiMVP projects and GVCC to develop EpiPred. EpiPred (epilepsy variant prediction) is a machine learning model that will allow accurate classification of missense variants as likely pathogenic or benign for epilepsy-associated genes.<\/p>\n","protected":false},"featured_media":276,"template":"","acf":[],"_links":{"self":[{"href":"https:\/\/epimvp.med.umich.edu\/wp-json\/wp\/v2\/creative_projects\/179"}],"collection":[{"href":"https:\/\/epimvp.med.umich.edu\/wp-json\/wp\/v2\/creative_projects"}],"about":[{"href":"https:\/\/epimvp.med.umich.edu\/wp-json\/wp\/v2\/types\/creative_projects"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/epimvp.med.umich.edu\/wp-json\/wp\/v2\/media\/276"}],"wp:attachment":[{"href":"https:\/\/epimvp.med.umich.edu\/wp-json\/wp\/v2\/media?parent=179"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}