ArticleBiology direct2019
Robust pathway-based multi-omics data integration using directed random walks for survival prediction in multiple cancer studies.
Article in Biology direct, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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32 citing papers in PubMed.
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- A novel non-negative Bayesian stacking modeling method for Cancer survival prediction using high-dimensional omics data.BMC medical research methodology · 2024Article
- Associations between genetically predicted plasma protein levels and Alzheimer's disease risk: a study using genetic prediction models.Alzheimer's research & therapy · 2024Article
- Proteome-wide association study and functional validation identify novel protein markers for pancreatic ductal adenocarcinoma.GigaScience · 2024Article
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- Pancancer survival prediction using a deep learning architecture with multimodal representation and integration.Bioinformatics advances · 2023Article
- InterTADs: integration of multi-omics data on topologically associated domains, application to chronic lymphocytic leukemia.NAR genomics and bioinformatics · 2022Article
- The p53 family member p73 in the regulation of cell stress response.Biology direct · 2021Review
- Global mapping of cancers: The Cancer Genome Atlas and beyond.Molecular oncology · 2021Review
- Serine and one-carbon metabolisms bring new therapeutic venues in prostate cancer.Discover oncology · 2021Review
- Emerging roles of the HECT-type E3 ubiquitin ligases in hematological malignancies.Discover oncology · 2021Review
- HiG2Vec: hierarchical representations of Gene Ontology and genes in the Poincaré ball.Bioinformatics (Oxford, England) · 2021Article
- Central vascular ligation and mesentery based abdominal surgery.Discover oncology · 2021Review
- Low level of plasminogen increases risk for mortality in COVID-19 patients.Cell death & disease · 2021Article
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Abstract
backgroundIntegrating the rich information from multi-omics data has been a popular approach to survival prediction and bio-marker identification for several cancer studies. To facilitate the integrative analysis of multiple genomic profiles, several studies have suggested utilizing pathway information rather than using individual genomic profiles.
methodsWe have recently proposed an integrative directed random walk-based method utilizing pathway information (iDRW) for more robust and effective genomic feature extraction. In this study, we applied iDRW to multiple genomic profiles for two different cancers, and designed a directed gene-gene graph which reflects the interaction between gene expression and copy number data. In the experiments, the performances of the iDRW method and four state-of-the-art pathway-based methods were compared using a survival prediction model which classifies samples into two survival groups.
resultsThe results show that the integrative analysis guided by pathway information not only improves prediction performance, but also provides better biological insights into the top pathways and genes prioritized by the model in both the neuroblastoma and the breast cancer datasets. The pathways and genes selected by the iDRW method were shown to be related to the corresponding cancers.
conclusionsIn this study, we demonstrated the effectiveness of a directed random walk-based multi-omics data integration method applied to gene expression and copy number data for both breast cancer and neuroblastoma datasets. We revamped a directed gene-gene graph considering the impact of copy number variation on gene expression and redefined the weight initialization and gene-scoring method. The benchmark result for iDRW with four pathway-based methods demonstrated that the iDRW method improved survival prediction performance and jointly identified cancer-related pathways and genes for two different cancer datasets. REVIEWERS: This article was reviewed by Helena Molina-Abril and Marta Hidalgo.
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