ArticleBMC medical genomics2018
Integrative pathway-based survival prediction utilizing the interaction between gene expression and DNA methylation in breast cancer.
Article in BMC medical genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Visible neural networks for multi-omics integration: a critical review.Frontiers in artificial intelligence · 2025Pooled it
- Multi-task adaptive deep sparse canonical correlation analysis for multi-omics cancer survival prediction.PloS one · 2026Article
- A review of the use of tumour DNA methylation for breast cancer subtyping and prediction of outcomes.Clinical epigenetics · 2025Review
- Article
- ReGeNNe: genetic pathway-based deep neural network using canonical correlation regularizer for disease prediction.Bioinformatics (Oxford, England) · 2023Article
- Multi-Omics Data Analysis Identifies Prognostic Biomarkers across Cancers.Medical sciences (Basel, Switzerland) · 2023Article
- Pancancer survival prediction using a deep learning architecture with multimodal representation and integration.Bioinformatics advances · 2023Article
- A five-pseudouridylation-associated-LncRNA classifier for primary prostate cancer prognosis prediction.Frontiers in genetics · 2022Article
- IMPDH1, a prognostic biomarker and immunotherapy target that correlates with tumor immune microenvironment in pan-cancer and hepatocellular carcinoma.Frontiers in immunology · 2022Article
- Unsupervised Multi-Omics Data Integration Methods: A Comprehensive Review.Frontiers in genetics · 2022Review
- Uncovering the prognostic gene signatures for the improvement of risk stratification in cancers by using deep learning algorithm coupled with wavelet transform.BMC bioinformatics · 2020Article
- Topological integration of RPPA proteomic data with multi-omics data for survival prediction in breast cancer via pathway activity inference.BMC medical genomics · 2019Article
- Machine learning with the TCGA-HNSC dataset: improving usability by addressing inconsistency, sparsity, and high-dimensionality.BMC bioinformatics · 2019Article
- Robust pathway-based multi-omics data integration using directed random walks for survival prediction in multiple cancer studies.Biology direct · 2019Article
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Authors and funding
4 authors.
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Abstract
backgroundIntegrative analysis on multi-omics data has gained much attention recently. To investigate the interactive effect of gene expression and DNA methylation on cancer, we propose a directed random walk-based approach on an integrated gene-gene graph that is guided by pathway information.
methodsOur approach first extracts a single pathway profile matrix out of the gene expression and DNA methylation data by performing the random walk over the integrated graph. We then apply a denoising autoencoder to the pathway profile to further identify important pathway features and genes. The extracted features are validated in the survival prediction task for breast cancer patients.
resultsThe results show that the proposed method substantially improves the survival prediction performance compared to that of other pathway-based prediction methods, revealing that the combined effect of gene expression and methylation data is well reflected in the integrated gene-gene graph combined with pathway information. Furthermore, we show that our joint analysis on the methylation features and gene expression profile identifies cancer-specific pathways with genes related to breast cancer.
conclusionsIn this study, we proposed a DRW-based method on an integrated gene-gene graph with expression and methylation profiles in order to utilize the interactions between them. The results showed that the constructed integrated gene-gene graph can successfully reflect the combined effect of methylation features on gene expression profiles. We also found that the selected features by DA can effectively extract topologically important pathways and genes specifically related to breast cancer.
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