Show pageBacklinksCite current pageExport to PDFBack to top This page is read only. You can view the source, but not change it. Ask your administrator if you think this is wrong. ====== EpiGe ====== EpiGe-App https://www.epige.irsjd.org/ Gómez-González et al. present a [[machine learning decision support system]] (DSS) that enables the classification of the principal molecular groups WNT, SHH, and non-WNT/non-SHH directly from quantitative PCR (qPCR) data. We propose a framework where the developed DSS appears as a user-friendly web-application-EpiGe-App that enables automated interpretation of qPCR methylation data and subsequent molecular group prediction. The basis of our classification strategy is a previously validated six-cytosine signature with subgroup-specific methylation profiles. This reduced set of markers enabled us to develop a methyl-genotyping assay capable of determining the methylation status of cytosines using qPCR instruments. This study provides a comprehensive approach for the rapid classification of clinically relevant medulloblastoma groups, using readily accessible equipment and an easy-to-use web application ((Gómez-González S, Llano J, Garcia M, Garrido-Garcia A, Suñol M, Lemos I, Perez-Jaume S, Salvador N, Gene-Olaciregui N, Galán RA, Santa-María V, Perez-Somarriba M, Castañeda A, Hinojosa J, Winter U, Moreira FB, Lubieniecki F, Vazquez V, Mora J, Cruz O, La Madrid AM, Perera A, Lavarino C. EpiGe: A machine-learning strategy for rapid classification of medulloblastoma using PCR-based methyl-genotyping. iScience. 2023 Aug 12;26(9):107598. doi: 10.1016/j.isci.2023.107598. PMID: 37664618; PMCID: PMC10470382.)). epige.txt Last modified: 2024/06/07 02:53by 127.0.0.1