ArticleGenes2019
Classifying Breast Cancer Subtypes Using Multiple Kernel Learning Based on Omics Data.
Article in Genes, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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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
21 citing papers in PubMed.
- End-to-End Multimodal Multiple Instance Learning for Cancer Histopathology Classification with Dual-Attention Fusion.Journal of medical systems · 2026Article
- AHDSN: an attention-enabled hybrid deep sequential network for cancer survivability prediction from multi-omics data.Mammalian genome : official journal of the International Mammalian Genome Society · 2025Article
- KYNU is a potential metabolic-related biomarker for nasopharyngeal carcinoma by Raman spectroscopy, metabolomics, and transcriptomics analysis.Discover oncology · 2025Article
- A review of the use of tumour DNA methylation for breast cancer subtyping and prediction of outcomes.Clinical epigenetics · 2025Review
- Classifying breast cancer subtypes on multi-omics data via sparse canonical correlation analysis and deep learning.BMC bioinformatics · 2024Article
- Classifying breast cancer using multi-view graph neural network based on multi-omics data.Frontiers in genetics · 2024Article
- moBRCA-net: a breast cancer subtype classification framework based on multi-omics attention neural networks.BMC bioinformatics · 2023Article
- Integration of multi-omics data reveals a novel hybrid breast cancer subtype and its biomarkers.Frontiers in oncology · 2023Article
- Heterogeneous data integration methods for patient similarity networks.Briefings in bioinformatics · 2022Review
- Leveraging Deep Learning Techniques and Integrated Omics Data for Tailored Treatment of Breast Cancer.Journal of personalized medicine · 2022Article
- Deep-Learning-Based Cancer Profiles Classification Using Gene Expression Data Profile.Journal of healthcare engineering · 2022Article
- Biomedical Application of Identified Biomarkers Gene Expression Based Early Diagnosis and Detection in Cervical Cancer with Modified Probabilistic Neural Network.Contrast media & molecular imaging · 2022Article
- Article
- Development of CDK4/6 Inhibitors: A Five Years Update.Molecules (Basel, Switzerland) · 2021Review
- MONTI: A Multi-Omics Non-negative Tensor Decomposition Framework for Gene-Level Integrative Analysis.Frontiers in genetics · 2021Article
- Integration strategies of multi-omics data for machine learning analysis.Computational and structural biotechnology journal · 2021Review
- Classifying Breast Cancer Subtypes Using Deep Neural Networks Based on Multi-Omics Data.Genes · 2020Article
- eBreCaP: extreme learning-based model for breast cancer survival prediction.IET systems biology · 2020Article
- Hormone Receptor-Status Prediction in Breast Cancer Using Gene Expression Profiles and Their Macroscopic Landscape.Cancers · 2020Article
- Article
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
No grant is acknowledged in the PubMed record.
Abstract
It is very significant to explore the intrinsic differences in breast cancer subtypes. These intrinsic differences are closely related to clinical diagnosis and designation of treatment plans. With the accumulation of biological and medicine datasets, there are many different omics data that can be viewed in different aspects. Combining these multiple omics data can improve the accuracy of prediction. Meanwhile; there are also many different databases available for us to download different types of omics data. In this article, we use estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) to define breast cancer subtypes and classify any two breast cancer subtypes using SMO-MKL algorithm. We collected mRNA data, methylation data and copy number variation (CNV) data from TCGA to classify breast cancer subtypes. Multiple Kernel Learning (MKL) is employed to use these omics data distinctly. The result of using three omics data with multiple kernels is better than that of using single omics data with multiple kernels. Furthermore; these significant genes and pathways discovered in the feature selection process are also analyzed. In experiments; the proposed method outperforms other state-of-the-art methods and has abundant biological interpretations.
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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.