ArticleFrontiers in genetics2020
Distinguishing Glioblastoma Subtypes by Methylation Signatures.
Article in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed, 36 citations in OpenAlex.
- Comprehensive Analysis of scRNA-Seq and RNA-Seq Revealed the Role of Programmed Cell Death-Related Genes in the Prognosis of Glioblastoma Patients.Applied biochemistry and biotechnology · 2026Article
- Prediction of Solubility of Proteins in Escherichia coli Based on Functional and Structural Features Using Machine Learning Methods.The protein journal · 2024Article
- Genetic Prognostic Factors in Adult Diffuse Gliomas: A 10-Year Experience at a Single Institution.Cancers · 2024Article
- Molecular diversity in isocitrate dehydrogenase-wild-type glioblastoma.Brain communications · 2024Review
- DNA methylation alterations at RE1-silencing transcription factor binding sites and their flanking regions in cancer.Clinical epigenetics · 2023Article
- Identification of Phase-Separation-Protein-Related Function Based on Gene Ontology by Using Machine Learning Methods.Life (Basel, Switzerland) · 2023Article
- Machine Learning Classification of Time since BNT162b2 COVID-19 Vaccination Based on Array-Measured Antibody Activity.Life (Basel, Switzerland) · 2023Article
- Identification of genes related to immune enhancement caused by heterologous ChAdOx1-BNT162b2 vaccines in lymphocytes at single-cell resolution with machine learning methods.Frontiers in immunology · 2023Article
- Characterization of chromatin accessibility patterns in different mouse cell types using machine learning methods at single-cell resolution.Frontiers in genetics · 2023Article
- Identification of Smoking-Associated Transcriptome Aberration in Blood with Machine Learning Methods.BioMed research international · 2023Article
- Decoding molecular programs in melanoma brain metastases.Nature communications · 2022Article
- Identifying MicroRNA Markers That Predict COVID-19 Severity Using Machine Learning Methods.Life (Basel, Switzerland) · 2022Article
- MGMT and Whole-Genome DNA Methylation Impacts on Diagnosis, Prognosis and Therapy of Glioblastoma Multiforme.International journal of molecular sciences · 2022Review
- Identifying In Vitro Cultured Human Hepatocytes Markers with Machine Learning Methods Based on Single-Cell RNA-Seq Data.Frontiers in bioengineering and biotechnology · 2022Article
- The complex interactions between the cellular and non-cellular components of the brain tumor microenvironmental landscape and their therapeutic implications.Frontiers in oncology · 2022Review
- Survival and quality of life analysis in glioblastoma multiforme with adjuvant chemoradiotherapy: a retrospective study.Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation Oncology · 2022Article
- Identifying anal and cervical tumorigenesis-associated methylation signaling with machine learning methods.Frontiers in oncology · 2022Article
- Identification of COVID-19-Specific Immune Markers Using a Machine Learning Method.Frontiers in molecular biosciences · 2022Article
- Identification of cortical interneuron cell markers in mouse embryos based on machine learning analysis of single-cell transcriptomics.Frontiers in neuroscience · 2022Article
- Immune landscape-based machine-learning-assisted subclassification, prognosis, and immunotherapy prediction for glioblastoma.Frontiers in immunology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 8 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Glioblastoma, also called glioblastoma multiform (GBM), is the most aggressive cancer that initiates within the brain. GBM is produced in the central nervous system. Cancer cells in GBM are similar to stem cells. Several different schemes for GBM stratification exist. These schemes are based on intertumoral molecular heterogeneity, preoperative images, and integrated tumor characteristics. Although the formation of glioblastoma is remarkably related to gene methylation, GBM has been poorly classified by epigenetics. To classify glioblastoma subtypes on the basis of different degrees of genes' methylation, we adopted several powerful machine learning algorithms to identify numerous methylation features (sites) associated with the classification of GBM. The features were first analyzed by an excellent feature selection method, Monte Carlo feature selection (MCFS), resulting in a feature list. Then, such list was fed into the incremental feature selection (IFS), incorporating one classification algorithm, to extract essential sites. These sites can be annotated onto coding genes, such as
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.