ArticleJournal of clinical medicine2018
Identification of the Gene Expression Rules That Define the Subtypes in Glioma.
Article in Journal of clinical medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
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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.
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Who cites it
26 citing papers in PubMed, 46 citations in OpenAlex.
- Machine Learning-Based Classification of Gliomas and Tumor Grades with SHAP-Guided Feature Interpretation.Genes · 2026Article
- From Data to Decision: Integrating Bioinformatics into Glioma Patient Stratification and Immunotherapy Selection.International journal of molecular sciences · 2026Review
- Sprouty in Tumors of the Nervous System.International journal of molecular sciences · 2025Review
- Explainable Machine Learning Models for Glioma Subtype Classification and Survival Prediction.Cancers · 2025Article
- DeepAutoGlioma: a deep learning autoencoder-based multi-omics data integration and classification tools for glioma subtyping.BioData mining · 2023Article
- Transcription Factor BCL11A Regulates Schwann Cell Behavior During Peripheral Nerve Regeneration.Molecular neurobiology · 2023Article
- Circular RNA hsa_circ_0000285 regulates the microRNA-599/G-protein subunit gamma 12 (miR-599/GNG12) axis to promote glioma progression.Journal of clinical laboratory analysis · 2022Article
- GNG12 Targeted by miR-876-5p Contributes to Glioma Progression Through the Activation of the PI3K/AKT Signaling Pathway.Journal of molecular neuroscience : MN · 2022Article
- Identification of Novel Key Genes and Pathways in Multiple Sclerosis Based on Weighted Gene Coexpression Network Analysis and Long Noncoding RNA-Associated Competing Endogenous RNA Network.Oxidative medicine and cellular longevity · 2022Article
- Low GNG12 Expression Predicts Adverse Outcomes: A Potential Therapeutic Target for Osteosarcoma.Frontiers in immunology · 2021Article
- Article
- Open Data for Differential Network Analysis in Glioma.International journal of molecular sciences · 2020Article
- Identifying Cell-Type Specific Genes and Expression Rules Based on Single-Cell Transcriptomic Atlas Data.Frontiers in bioengineering and biotechnology · 2020Article
- Identification and Analysis of Dysfunctional Genes and Pathways in CD8Frontiers in genetics · 2020Article
- Alternative Polyadenylation Modification Patterns Reveal Essential Posttranscription Regulatory Mechanisms of Tumorigenesis in Multiple Tumor Types.BioMed research international · 2020Article
- Prognostic and Predictive Value of a Long Non-coding RNA Signature in Glioma: A lncRNA Expression Analysis.Frontiers in oncology · 2020Article
- Inferring novel genes related to oral cancer with a network embedding method and one-class learning algorithms.Gene therapy · 2019Article
- BCL11A: a potential diagnostic biomarker and therapeutic target in human diseases.Bioscience reports · 2019Review
- Analysis of Expression Pattern of snoRNAs in Different Cancer Types with Machine Learning Algorithms.International journal of molecular sciences · 2019Article
- Immune contexture defined by single cell technology for prognosis prediction and immunotherapy guidance in cancer.Cancer communications (London, England) · 2019Review
Corrections and comments
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Authors and funding
8 authors at 5 institutions in 3 countries.
Funding
Abstract
As a common brain cancer derived from glial cells, gliomas have three subtypes: glioblastoma, diffuse astrocytoma, and anaplastic astrocytoma. The subtypes have distinctive clinical features but are closely related to each other. A glioblastoma can be derived from the early stage of diffuse astrocytoma, which can be transformed into anaplastic astrocytoma. Due to the complexity of these dynamic processes, single-cell gene expression profiles are extremely helpful to understand what defines these subtypes. We analyzed the single-cell gene expression profiles of 5057 cells of anaplastic astrocytoma tissues, 261 cells of diffuse astrocytoma tissues, and 1023 cells of glioblastoma tissues with advanced machine learning methods. In detail, a powerful feature selection method, Monte Carlo feature selection (MCFS) method, was adopted to analyze the gene expression profiles of cells, resulting in a feature list. Then, the incremental feature selection (IFS) method was applied to the obtained feature list, with the help of support vector machine (SVM), to extract key features (genes) and construct an optimal SVM classifier. Several key biomarker genes, such as
Indexed as
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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.