ArticleCancers2022
Deep Learning-Based Pan-Cancer Classification Model Reveals Tissue-of-Origin Specific Gene Expression Signatures.
Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- Keratin gene expression signature predicts prognosis and immunotherapy efficacy in lung adenocarcinoma.Oncology letters · 2026Article
- Integrating artificial intelligence into cancers of unknown primary diagnosis and treatment.iScience · 2026Review
- DNA methylation biomarkers-based pan-cancer classifier: predictive modeling for cancer classification.Genome medicine · 2026Article
- Leveraging population-scale proteomic data with deep learning for head and neck cancer detection in saliva.NPJ digital medicine · 2026Article
- Systematic identification of pan-cancer single-gene expression biomarkers in drug high-throughput screens.PloS one · 2026Article
- ACmix-Swin Deep Learning of 4-Day-OldGenes · 2025Article
- Precision Oncology: Current Landscape, Emerging Trends, Challenges, and Future Perspectives.Cells · 2025Review
- Spatial Modularity of Innate Immune Networks Across Bactrian Camel Tissues.Animals : an open access journal from MDPI · 2025Article
- An interpretable hybrid deep learning framework for gastric cancer diagnosis using histopathological imaging.Scientific reports · 2025Article
- Learning the cellular origins across cancers using single-cell chromatin landscapes.Nature communications · 2025Article
- HallmarkGraph: a cancer hallmark informed graph neural network for classifying hierarchical tumor subtypes.Bioinformatics (Oxford, England) · 2025Article
- Pan-cancer analysis shapes the understanding of cancer biology and medicine.Cancer communications (London, England) · 2025Review
- DOMSCNet: a deep learning model for the classification of stomach cancer using multi-layer omics data.Briefings in bioinformatics · 2025Article
- Predicting Treatment Outcomes in Patients with Low Back Pain Using Gene Signature-Based Machine Learning Models.Pain and therapy · 2025Article
- Advancing precision oncology with AI-powered genomic analysis.Frontiers in pharmacology · 2025Review
- Computational models for pan-cancer classification based on multi-omics data.Frontiers in genetics · 2025Review
- Biologically-informed regional subset analysis with CatBoost for robust tissue-of-origin prediction.PloS one · 2025Article
- A review on trends in development and translation of omics signatures in cancer.Computational and structural biotechnology journal · 2024Review
- A Comprehensive Review of Bioinformatics Tools for Genomic Biomarker Discovery Driving Precision Oncology.Genes · 2024Review
- Application of Transcriptome-Based Gene Set Featurization for Machine Learning Model to Predict the Origin of Metastatic Cancer.Current issues in molecular biology · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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Abstract
Cancer tissue-of-origin specific biomarkers are needed for effective diagnosis, monitoring, and treatment of cancers. In this study, we analyzed transcriptomics data from 37 cancer types provided by The Cancer Genome Atlas (TCGA) to identify cancer tissue-of-origin specific gene expression signatures. We developed a deep neural network model to classify cancers based on gene expression data. The model achieved a predictive accuracy of >97% across cancer types indicating the presence of distinct cancer tissue-of-origin specific gene expression signatures. We interpreted the model using Shapley additive explanations to identify specific gene signatures that significantly contributed to cancer-type classification. We evaluated the model and the validity of gene signatures using an independent test data set from the International Cancer Genome Consortium. In conclusion, we present a robust neural network model for accurate classification of cancers based on gene expression data and also provide a list of gene signatures that are valuable for developing biomarker panels for determining cancer tissue-of-origin. These gene signatures serve as valuable biomarkers for determining tissue-of-origin for cancers of unknown primary.
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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.