ArticleJournal of the American Medical Informatics Association : JAMIA2015
Knowledge boosting: a graph-based integration approach with multi-omics data and genomic knowledge for cancer clinical outcome prediction.
Article in Journal of the American Medical Informatics Association : JAMIA, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
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Who cites it
48 citing papers in PubMed.
- PEARL: integrative multi-omics classification and omics feature discovery via deep graph learning.Bioinformatics (Oxford, England) · 2026Article
- Construction and Applications of Knowledge Graphs in Ophthalmology.Ophthalmic research · 2026Review
- Article
- DriverOmicsNet: an integrated graph convolutional network for multi-omics exploration of cancer driver genes.Briefings in bioinformatics · 2025Article
- Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A Review.IEEE transactions on automation science and engineering : a publication of the IEEE Robotics and Automation Society · 2025Article
- Supervised graph contrastive learning for cancer subtype identification through multi-omics data integration.Health information science and systems · 2024Article
- Web-based multi-omics integration using the Analyst software suite.Nature protocols · 2024Review
- Combining phenotypic and genomic data to improve prediction of binary traits.Journal of applied statistics · 2024Article
- The SciQA Scientific Question Answering Benchmark for Scholarly Knowledge.Scientific reports · 2023Article
- A Machine Learning-Based Approach Using Multi-omics Data to Predict Metabolic Pathways.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Heterogeneous data integration methods for patient similarity networks.Briefings in bioinformatics · 2022Review
- Machine learning: its challenges and opportunities in plant system biology.Applied microbiology and biotechnology · 2022Review
- Ontologies and Knowledge Graphs in Oncology Research.Cancers · 2022Review
- Interpretation of network-based integration from multi-omics longitudinal data.Nucleic acids research · 2022Article
- High-dimensional role of AI and machine learning in cancer research.British journal of cancer · 2022Review
- Integrated Multi-Omics Analysis Model to Identify Biomarkers Associated With Prognosis of Breast Cancer.Frontiers in oncology · 2022Article
- Epigenetic interplay between methylation and miRNA in bladder cancer: focus on isoform expression.BMC genomics · 2021Article
- Functional network estimation using multigraph learning with application to brain maturation study.Human brain mapping · 2021Article
- MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification.Nature communications · 2021Article
- Prognostic Effect of Inflammatory Genes on Stage I-III Colorectal Cancer-Integrative Analysis of TCGA Data.Cancers · 2021Article
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Authors and funding
7 authors.
Funding
Abstract
objectiveCancer can involve gene dysregulation via multiple mechanisms, so no single level of genomic data fully elucidates tumor behavior due to the presence of numerous genomic variations within or between levels in a biological system. We have previously proposed a graph-based integration approach that combines multi-omics data including copy number alteration, methylation, miRNA, and gene expression data for predicting clinical outcome in cancer. However, genomic features likely interact with other genomic features in complex signaling or regulatory networks, since cancer is caused by alterations in pathways or complete processes.
methodsHere we propose a new graph-based framework for integrating multi-omics data and genomic knowledge to improve power in predicting clinical outcomes and elucidate interplay between different levels. To highlight the validity of our proposed framework, we used an ovarian cancer dataset from The Cancer Genome Atlas for predicting stage, grade, and survival outcomes.
resultsIntegrating multi-omics data with genomic knowledge to construct pre-defined features resulted in higher performance in clinical outcome prediction and higher stability. For the grade outcome, the model with gene expression data produced an area under the receiver operating characteristic curve (AUC) of 0.7866. However, models of the integration with pathway, Gene Ontology, chromosomal gene set, and motif gene set consistently outperformed the model with genomic data only, attaining AUCs of 0.7873, 0.8433, 0.8254, and 0.8179, respectively.
conclusionsIntegrating multi-omics data and genomic knowledge to improve understanding of molecular pathogenesis and underlying biology in cancer should improve diagnostic and prognostic indicators and the effectiveness of therapies.
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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.