A Deep Learning-Based Framework for Supporting Clinical Diagnosis of Glioblastoma Subtypes.

ABSTRACT: Understanding molecular features that facilitate aggressive phenotypes in glioblastoma multiforme (GBM) remains a major clinical challenge. Accurate diagnosis of GBM subtypes, namely classical, proneural, and mesenchymal, and identification of specific molecular features are crucial for clinicians for systematic treatment. We develop a biologically interpretable and highly efficient deep learning framework based on a convolutional neural network for subtype identification. The classifiers were generated from high-throughput data of different molecular levels, i.e., transcriptome and methylome. Furthermore, an integrated subsystem of transcriptome and methylome data was also used to build the biologically relevant model. Our results show that deep learning model outperforms the traditional

SUBMITTER: Munquad S 

PROVIDER: S-EPMC9000988 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature