ArticleFrontiers in genetics2019
Discovering Cancer Subtypes via an Accurate Fusion Strategy on Multiple Profile Data.
Article in Frontiers in genetics, 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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Who cites it
21 citing papers in PubMed.
- KGRDR: a deep learning model based on knowledge graph and graph regularized integration for drug repositioning.Frontiers in pharmacology · 2025Article
- Elucidating Cancer Subtypes by Using the Relationship between DNA Methylation and Gene Expression.Genes · 2024Article
- Multi-omics subgroups associated with glycaemic deterioration in type 2 diabetes: an IMI-RHAPSODY Study.Frontiers in endocrinology · 2024Article
- Recursive integration of synergised graph representations of multi-omics data for cancer subtypes identification.Scientific reports · 2022Article
- Pan-cancer integrative analysis of whole-genome De novo somatic point mutations reveals 17 cancer types.BMC bioinformatics · 2022Article
- Heterogeneous data integration methods for patient similarity networks.Briefings in bioinformatics · 2022Review
- Mucin Expression and Splicing Determine Novel Subtypes and Patient Mortality in Pancreatic Ductal Adenocarcinoma.Clinical cancer research : an official journal of the American Association for Cancer Research · 2021Article
- Molecular Subtypes of Oral Squamous Cell Carcinoma Based on Immunosuppression Genes Using a Deep Learning Approach.Frontiers in cell and developmental biology · 2021Article
- Multi-Omics Data Fusion via a Joint Kernel Learning Model for Cancer Subtype Discovery and Essential Gene Identification.Frontiers in genetics · 2021Article
- ILPMDA: Predicting miRNA-Disease Association Based on Improved Label Propagation.Frontiers in genetics · 2021Article
- Application of Sparse Representation in Bioinformatics.Frontiers in genetics · 2021Review
- Prediction of tumour pathological subtype from genomic profile using sparse logistic regression with random effects.Journal of applied statistics · 2021Article
- Prediction of age at onset in Parkinson's disease using objective specific neuroimaging genetics based on a sparse canonical correlation analysis.Scientific reports · 2020Article
- MSFSP: A Novel miRNA-Disease Association Prediction Model by Federating Multiple-Similarities Fusion and Space Projection.Frontiers in genetics · 2020Article
- Kernel Fusion Method for Detecting Cancer Subtypes via Selecting Relevant Expression Data.Frontiers in genetics · 2020Article
- PredAmyl-MLP: Prediction of Amyloid Proteins Using Multilayer Perceptron.Computational and mathematical methods in medicine · 2020Article
- Computational Methods for Identifying Similar Diseases.Molecular therapy. Nucleic acids · 2019Review
- Selecting Essential MicroRNAs Using a Novel Voting Method.Molecular therapy. Nucleic acids · 2019Article
- Article
- An Ensemble Strategy to Predict Prognosis in Ovarian Cancer Based on Gene Modules.Frontiers in genetics · 2019Article
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Discovering cancer subtypes is useful for guiding clinical treatment of multiple cancers. Progressive profile technologies for tissue have accumulated diverse types of data. Based on these types of expression data, various computational methods have been proposed to predict cancer subtypes. It is crucial to study how to better integrate these multiple profiles of data. In this paper, we collect multiple profiles of data for five cancers on The Cancer Genome Atlas (TCGA). Then, we construct three similarity kernels for all patients of the same cancer by gene expression, miRNA expression and isoform expression data. We also propose a novel unsupervised multiple kernel fusion method, Similarity Kernel Fusion (SKF), in order to integrate three similarity kernels into one combined kernel. Finally, we make use of spectral clustering on the integrated kernel to predict cancer subtypes. In the experimental results, the
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