EpiGe: A machine-learning strategy for rapid classification of medulloblastoma using PCR-based methyl-genotyping.

ABSTRACT: Molecular classification of medulloblastoma is critical for the treatment of this brain tumor. Array-based DNA methylation profiling has emerged as a powerful approach for brain tumor classification. However, this technology is currently not widely available. We 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.

SUBMITTER: Gomez-Gonzalez S 

PROVIDER: S-EPMC10470382 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature