{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["23(1)"],"submitter":["Tian J"],"pubmed_abstract":["<h4>Background</h4>Gliomas are highly complex and heterogeneous tumors, rendering prognosis prediction challenging. The advent of deep learning algorithms and the accessibility of multi-omic data represent a new approach for the identification of survival-sensitive subtypes. Herein, an autoencoder-based approach was used to identify two survival-sensitive subtypes using RNA sequencing (RNA-seq) and DNA methylation (DNAm) data from The Cancer Genome Atlas (TCGA) dataset. The subtypes were used as labels to build a support vector machine model with cross-validation. We validated the robustness of the model on Chinese Glioma Genome Atlas (CGGA) dataset. DNAm-driven genes were identified by integrating DNAm and gene expression profiling analyses using the R MethylMix package and carried out fo"],"journal":["BMC bioinformatics"],"pagination":["417"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9552440"],"repository":["biostudies-literature"],"pubmed_title":["Deep learning algorithm reveals two prognostic subtypes in patients with gliomas."],"pmcid":["PMC9552440"],"pubmed_authors":["Tian J","Peng X","Zhu M","Wang P","Ren Z","Zhao Q","Wu B","Zhang M","Feng R","Fu M","He CK"],"additional_accession":[]},"is_claimable":false,"name":"Deep learning algorithm reveals two prognostic subtypes in patients with gliomas.","description":"<h4>Background</h4>Gliomas are highly complex and heterogeneous tumors, rendering prognosis prediction challenging. The advent of deep learning algorithms and the accessibility of multi-omic data represent a new approach for the identification of survival-sensitive subtypes. Herein, an autoencoder-based approach was used to identify two survival-sensitive subtypes using RNA sequencing (RNA-seq) and DNA methylation (DNAm) data from The Cancer Genome Atlas (TCGA) dataset. The subtypes were used as labels to build a support vector machine model with cross-validation. We validated the robustness of the model on Chinese Glioma Genome Atlas (CGGA) dataset. DNAm-driven genes were identified by integrating DNAm and gene expression profiling analyses using the R MethylMix package and carried out fo","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Oct","modification":"2025-04-19T06:16:08.863Z","creation":"2025-02-19T01:02:11.557Z"},"accession":"S-EPMC9552440","cross_references":{"pubmed":["36221066"],"doi":["10.1186/s12859-022-04970-x"]}}