ArticleGenes2021
Identifying Cancer Subtypes Using a Residual Graph Convolution Model on a Sample Similarity Network.
Article in Genes, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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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
11 citing papers in PubMed.
- Knowledge-guided graph fusion of mRNA profiles for interpretable cancer subtyping.Briefings in functional genomics · 2026Article
- Article
- Uncovering the Understanding of the Concept of Patient Similarity in Cancer Research and Treatment: Scoping Review.Journal of medical Internet research · 2025Article
- CENP-A and centromere evolution in equids.Chromosome research : an international journal on the molecular, supramolecular and evolutionary aspects of chromosome biology · 2025Review
- DeepMoIC: multi-omics data integration via deep graph convolutional networks for cancer subtype classification.BMC genomics · 2024Article
- Supervised graph contrastive learning for cancer subtype identification through multi-omics data integration.Health information science and systems · 2024Article
- CAEM-GBDT: a cancer subtype identifying method using multi-omics data and convolutional autoencoder network.Frontiers in bioinformatics · 2024Article
- meth-SemiCancer: a cancer subtype classification framework via semi-supervised learning utilizing DNA methylation profiles.BMC bioinformatics · 2023Article
- RN-Autoencoder: Reduced Noise Autoencoder for classifying imbalanced cancer genomic data.Journal of biological engineering · 2023Article
- Omics Data and Data Representations for Deep Learning-Based Predictive Modeling.International journal of molecular sciences · 2022Review
- A classification method of gastric cancer subtype based on residual graph convolution network.Frontiers in genetics · 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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer subtype classification helps us to understand the pathogenesis of cancer and develop new cancer drugs, treatment from which patients would benefit most. Most previous studies detect cancer subtypes by extracting features from individual samples, ignoring their associations with others. We believe that the interactions of cancer samples can help identify cancer subtypes. This work proposes a cancer subtype classification method based on a residual graph convolutional network and a sample similarity network. First, we constructed a sample similarity network regarding cancer gene co-expression patterns. Then, the gene expression profiles of cancer samples as initial features and the sample similarity network were passed into a two-layer graph convolutional network (GCN) model. We introduced the initial features to the GCN model to avoid over-smoothing during the training process. Finally, the classification of cancer subtypes was obtained through a softmax activation function. Our model was applied to breast invasive carcinoma (BRCA), glioblastoma multiforme (GBM) and lung cancer (LUNG) datasets. The accuracy values of our model reached 82.58%, 85.13% and 79.18% for BRCA, GBM and LUNG, respectively, which outperformed the existing methods. The survival analysis of our results proves the significant clinical features of the cancer subtypes identified by our model. Moreover, we can leverage our model to detect the essential genes enriched in gene ontology (GO) terms and the biological pathways related to a cancer subtype.
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