ArticleBioinformatics (Oxford, England)2019
Using association signal annotations to boost similarity network fusion.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Multi-omics to study chronic respiratory diseases and viral infections.European respiratory review : an official journal of the European Respiratory Society · 2026Review
- Uncovering the Understanding of the Concept of Patient Similarity in Cancer Research and Treatment: Scoping Review.Journal of medical Internet research · 2025Article
- Multi-omics architecture of childhood obesity and metabolic dysfunction uncovers biological pathways and prenatal determinants.Nature communications · 2025Article
- KGRDR: a deep learning model based on knowledge graph and graph regularized integration for drug repositioning.Frontiers in pharmacology · 2025Article
- PartIES: a disease subtyping framework with Partition-level Integration using diffusion-Enhanced Similarities from multi-omics Data.Briefings in bioinformatics · 2024Article
- A metagene based similarity network fusion approach for multi-omics data integration identified novel subtypes in renal cell carcinoma.Briefings in bioinformatics · 2024Article
- wMKL: multi-omics data integration enables novel cancer subtype identification via weight-boosted multi-kernel learning.British journal of cancer · 2024Article
- Neurobiological Clusters Are Associated With Trajectories of Overall Psychopathology in Youth.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2023Article
- Whole Person Modeling: a transdisciplinary approach to mental health research.Discover mental health · 2023Review
- An integrated analysis of the cancer genome atlas data discovers a hierarchical association structure across thirty three cancer types.PLOS digital health · 2022Article
- NESM: a network embedding method for tumor stratification by integrating multi-omics data.G3 (Bethesda, Md.) · 2022Article
- Heterogeneous data integration methods for patient similarity networks.Briefings in bioinformatics · 2022Review
- Multi-omics data integration for subtype identification of Chinese lower-grade gliomas: A joint similarity network fusion approach.Computational and structural biotechnology journal · 2022Article
- Unsupervised Multi-Omics Data Integration Methods: A Comprehensive Review.Frontiers in genetics · 2022Review
- Identification of autophagic target RAB13 with small-molecule inhibitor in low-grade glioma via integrated multi-omics approaches coupled with virtual screening of traditional Chinese medicine databases.Cell proliferation · 2021Article
- Similarity-based health risk prediction using Domain Fusion and electronic health records data.Journal of biomedical informatics · 2021Article
- Computational analysis of fused co-expression networks for the identification of candidate cancer gene biomarkers.NPJ systems biology and applications · 2021Article
- Copy number variations primed lncRNAs deregulation contribute to poor prognosis in colorectal cancer.Aging · 2019Article
- Integrative clustering methods for multi-omics data.Wiley interdisciplinary reviews. Computational statisticsArticle
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4 authors.
Funding
Abstract
motivationRecent technology developments have made it possible to generate various kinds of omics data, which provides opportunities to better solve problems such as disease subtyping or disease mapping using more comprehensive omics data jointly. Among many developed data-integration methods, the similarity network fusion (SNF) method has shown a great potential to identify new disease subtypes through separating similar subjects using multi-omics data. SNF effectively fuses similarity networks with pairwise patient similarity measures from different types of omics data into one fused network using both shared and complementary information across multiple types of omics data.
resultsIn this article, we proposed an association-signal-annotation boosted similarity network fusion (ab-SNF) method, adding feature-level association signal annotations as weights aiming to up-weight signal features and down-weight noise features when constructing subject similarity networks to boost the performance in disease subtyping. In various simulation studies, the proposed ab-SNF outperforms the original SNF approach without weights. Most importantly, the improvement in the subtyping performance due to association-signal-annotation weights is amplified in the integration process. Applications to somatic mutation data, DNA methylation data and gene expression data of three cancer types from The Cancer Genome Atlas project suggest that the proposed ab-SNF method consistently identifies new subtypes in each cancer that more accurately predict patient survival and are more biologically meaningful. AVAILABILITY AND IMPLEMENTATION: The R package abSNF is freely available for downloading from https://github.com/pfruan/abSNF. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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