ArticleBioinformatics (Oxford, England)2020
Deep-learning approach to identifying cancer subtypes using high-dimensional genomic data.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers.
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Who cites it
54 citing papers in PubMed.
- Deep learning multi-omics integration identifies new molecular subtypes of lung cancer.BioData mining · 2026Article
- Triad-LMF: a hierarchical low-rank multimodal fusion framework for robust cancer subtype classification using multi-omics data.BMC bioinformatics · 2026Article
- Deep Clustering-Based Metabolic Stratification of Non-Small Cell Lung Cancer Patients Through Integration of Somatic Mutation Profile and Network Propagation Algorithm.Interdisciplinary sciences, computational life sciences · 2025Article
- MO-GCAN: multi-omics integration based on graph convolutional and attention networks.Bioinformatics (Oxford, England) · 2025Article
- A Comprehensive Review of Deep Learning Applications with Multi-Omics Data in Cancer Research.Genes · 2025Review
- Towards precision oncology: a multi-level cancer classification system integrating liquid biopsy and machine learning.BioData mining · 2025Article
- Classification of non-TCGA cancer samples to TCGA molecular subtypes using compact feature sets.Cancer cell · 2025Article
- A generative deep neural network for pan-digestive tract cancer survival analysis.BioData mining · 2025Article
- GAIN-BRCA: a graph-based AI-net framework for breast cancer subtype classification using multiomics data.Bioinformatics advances · 2025Article
- Role of AI in empowering and redefining the oncology care landscape: perspective from a developing nation.Frontiers in digital health · 2025Review
- VICatMix: variational Bayesian clustering and variable selection for discrete biomedical data.Bioinformatics advances · 2025Article
- Cancer molecular subtyping using limited multi-omics data with missingness.PLoS computational biology · 2024Article
- CBioProfiler: A Web and Standalone Pipeline for Cancer Biomarker and Subtype Characterization.Genomics, proteomics & bioinformatics · 2024Article
- Translation of Epigenetics in Cell-Free DNA Liquid Biopsy Technology and Precision Oncology.Current issues in molecular biology · 2024Review
- Article
- Fine-grained Patient Similarity Measuring using Contrastive Graph Similarity Networks.Proceedings. IEEE International Conference on Healthcare Informatics · 2024Article
- [Identification of breast cancer subtypes based on graph convolutional network].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Article
- Multi-fusion strategy network-guided cancer subtypes discovering based on multi-omics data.Frontiers in genetics · 2024Article
- Automated exploitation of deep learning for cancer patient stratification across multiple types.Bioinformatics (Oxford, England) · 2023Article
- ForestSubtype: a cancer subtype identifying approach based on high-dimensional genomic data and a parallel random forest.BMC bioinformatics · 2023Article
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Authors and funding
4 authors.
Funding
Abstract
motivationCancer subtype classification has the potential to significantly improve disease prognosis and develop individualized patient management. Existing methods are limited by their ability to handle extremely high-dimensional data and by the influence of misleading, irrelevant factors, resulting in ambiguous and overlapping subtypes.
resultsTo address the above issues, we proposed a novel approach to disentangling and eliminating irrelevant factors by leveraging the power of deep learning. Specifically, we designed a deep-learning framework, referred to as DeepType, that performs joint supervised classification, unsupervised clustering and dimensionality reduction to learn cancer-relevant data representation with cluster structure. We applied DeepType to the METABRIC breast cancer dataset and compared its performance to state-of-the-art methods. DeepType significantly outperformed the existing methods, identifying more robust subtypes while using fewer genes. The new approach provides a framework for the derivation of more accurate and robust molecular cancer subtypes by using increasingly complex, multi-source data. AVAILABILITY AND IMPLEMENTATION: An open-source software package for the proposed method is freely available at http://www.acsu.buffalo.edu/~yijunsun/lab/DeepType.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.