ArticleFrontiers in physics2020
Classification of Cancer Types Using Graph Convolutional Neural Networks.
Article in Frontiers in physics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 2 of them syntheses that pooled it.
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
47 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data.BMC bioinformatics · 2023Pooled it
- A meta-analysis of RNA-Seq studies to identify novel genes that regulate aging.Experimental gerontology · 2023Pooled it
- Image-Driven Multimodal Deep Learning for Skin Cancer Diagnosis: Cross-Attention Fusion of Dermoscopic Imaging Clinical Data and Knowledge Graphs.Sensors (Basel, Switzerland) · 2026Article
- HallmarkGraph: a cancer hallmark informed graph neural network for classifying hierarchical tumor subtypes.Bioinformatics (Oxford, England) · 2025Article
- Expression graph network framework for biomarker discovery.Briefings in bioinformatics · 2025Article
- Building simplified cancer subtyping and prediction models with glycan gene signatures.Cell reports methods · 2025Article
- Interpretable graph Kolmogorov-Arnold networks for multi-cancer classification and biomarker identification using multi-omics data.Scientific reports · 2025Article
- Deep learning-driven multi-omics analysis: enhancing cancer diagnostics and therapeutics.Briefings in bioinformatics · 2025Review
- GNNMutation: a heterogeneous graph-based framework for cancer detection.BMC bioinformatics · 2025Article
- Epigenetic ageing clocks: statistical methods and emerging computational challenges.Nature reviews. Genetics · 2025Review
- Strategies to include prior knowledge in omics analysis with deep neural networks.Patterns (New York, N.Y.) · 2025Review
- A comparative analysis of gene expression profiling by statistical and machine learning approaches.Bioinformatics advances · 2025Article
- Comparative Analysis of Multi-Omics Integration Using Graph Neural Networks for Cancer Classification.IEEE access : practical innovations, open solutions · 2025Article
- Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A Review.IEEE transactions on automation science and engineering : a publication of the IEEE Robotics and Automation Society · 2025Article
- Evaluation of Au(III) complexes as Plasmodium falciparum aquaglyceroporin (PfAQP) inhibitors by in silico and in vitro methods.Journal of biological inorganic chemistry : JBIC : a publication of the Society of Biological Inorganic Chemistry · 2024Article
- Fusing multiplex heterogeneous networks using graph attention-aware fusion networks.Scientific reports · 2024Article
- Identification of molecular subtypes of dementia by using blood-proteins interaction-aware graph propagational network.Briefings in bioinformatics · 2024Article
- Computational methods and biomarker discovery strategies for spatial proteomics: a review in immuno-oncology.Briefings in bioinformatics · 2024Review
- Machine-learning analysis reveals an important role for negative selection in shaping cancer aneuploidy landscapes.Genome biology · 2024Article
- Prior knowledge-guided multilevel graph neural network for tumor risk prediction and interpretation via multi-omics data integration.Briefings in bioinformatics · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
backgroundCancer has been a leading cause of death in the United States with significant health care costs. Accurate prediction of cancers at an early stage and understanding the genomic mechanisms that drive cancer development are vital to the improvement of treatment outcomes and survival rates, thus resulting in significant social and economic impacts. Attempts have been made to classify cancer types with machine learning techniques during the past two decades and deep learning approaches more recently.
resultsIn this paper, we established four models with graph convolutional neural network (GCNN) that use unstructured gene expressions as inputs to classify different tumor and non-tumor samples into their designated 33 cancer types or as normal. Four GCNN models based on a co-expression graph, co-expression+singleton graph, protein-protein interaction (PPI) graph, and PPI+singleton graph have been designed and implemented. They were trained and tested on combined 10,340 cancer samples and 731 normal tissue samples from The Cancer Genome Atlas (TCGA) dataset. The established GCNN models achieved excellent prediction accuracies (89.9-94.7%) among 34 classes (33 cancer types and a normal group).
conclusionNovel GCNN models have been established to predict cancer types or normal tissue based on gene expression profiles. We demonstrated the results from the TCGA dataset that these models can produce accurate classification (above 94%), using cancer-specific markers genes. The models and the source codes are publicly available and can be readily adapted to the diagnosis of cancer and other diseases by the data-driven modeling research community.
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.