ArticleComputational and structural biotechnology journal2020
An integrative deep learning framework for classifying molecular subtypes of breast cancer.
Article in Computational and structural biotechnology journal, 2020. 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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33 citing papers in PubMed.
- Leveraging population-scale proteomic data with deep learning for head and neck cancer detection in saliva.NPJ digital medicine · 2026Article
- The Roles of MicroRNAs, Oncogenes, and Tumor Suppressor Gene Molecular Subtypes of Breast Cancer: Therapeutic Potential of Pharmaceutical and Natural Products.International journal of breast cancer · 2026Review
- SubNExT: Towards accurate, efficient and robust gene expression classification for breast cancer subtyping.Computational and structural biotechnology journal · 2026Article
- Adaptive multi-omics integration framework for breast cancer survival analysis.Scientific reports · 2025Article
- DeepCMS: A Feature Selection-Driven Model for Cancer Molecular Subtyping with a Case Study on Testicular Germ Cell Tumors.Diagnostics (Basel, Switzerland) · 2025Article
- Multi-omic network inference from time-series data.NPJ systems biology and applications · 2025Article
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- Development of a prognostic multiomic biomarker of progression-free survival in advanced non-small cell lung cancer patients treated with first line immunotherapy.Scientific reports · 2025Article
- Identification of a 10-species microbial signature of inflammatory bowel disease by machine learning and external validation.Cell regeneration (London, England) · 2025Article
- Open challenges and opportunities in federated foundation models towards biomedical healthcare.BioData mining · 2025Review
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- BC-predict: mining of signal biomarkers and production of models for early-stage breast cancer subtyping and prognosis.Frontiers in bioinformatics · 2025Article
- A review on trends in development and translation of omics signatures in cancer.Computational and structural biotechnology journal · 2024Review
- Comparison of data fusion strategies for automated prostate lesion detection using mpMRI correlated with whole mount histology.Radiation oncology (London, England) · 2024Article
- Pathformer: a biological pathway informed transformer for disease diagnosis and prognosis using multi-omics data.Bioinformatics (Oxford, England) · 2024Article
- HyperTMO: a trusted multi-omics integration framework based on hypergraph convolutional network for patient classification.Bioinformatics (Oxford, England) · 2024Article
- A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis.Bioengineering (Basel, Switzerland) · 2024Review
- [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
- Historical perspective and future directions: computational science in immuno-oncology.Journal for immunotherapy of cancer · 2024Review
- Automated quantification of stromal tumour infiltrating lymphocytes is associated with prognosis in breast cancer.Virchows Archiv : an international journal of pathology · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Classification of breast cancer subtypes using multi-omics profiles is a difficult problem since the data sets are high-dimensional and highly correlated. Deep neural network (DNN) learning has demonstrated advantages over traditional methods as it does not require any hand-crafted features, but rather automatically extract features from raw data and efficiently analyze high-dimensional and correlated data. We aim to develop an integrative deep learning framework for classifying molecular subtypes of breast cancer. We collect copy number alteration and gene expression data measured on the same breast cancer patients from the Molecular Taxonomy of Breast Cancer International Consortium. We propose a deep learning model to integrate the omics datasets for predicting their molecular subtypes. The performance of our proposed DNN model is compared with some baseline models. Furthermore, we evaluate the misclassification of the subtypes using the learned deep features and explore their usefulness for clustering the breast cancer patients. We demonstrate that our proposed integrative deep learning model is superior to other deep learning and non-deep learning based models. Particularly, we get the best prediction result among the deep learning-based integration models when we integrate the two data sources using the concatenation layer in the models without sharing the weights. Using the learned deep features, we identify 6 breast cancer subgroups and show that Her2-enriched samples can be classified into more than one tumor subtype. Overall, the integrated model show better performance than those trained on individual data sources.
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