ArticleBMC bioinformatics2015
MVDA: a multi-view genomic data integration methodology.
Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- PEARL: integrative multi-omics classification and omics feature discovery via deep graph learning.Bioinformatics (Oxford, England) · 2026Article
- Multi-View Integrative Approach For Imputing Short-Chain Fatty Acids and Identifying Key factors predicting Blood SCFA.bioRxiv : the preprint server for biology · 2024Article
- 3PNMF-MKL: A non-negative matrix factorization-based multiple kernel learning method for multi-modal data integration and its application to gene signature detection.Frontiers in genetics · 2023Article
- Article
- Network approaches for modeling the effect of drugs and diseases.Briefings in bioinformatics · 2022Review
- Unsupervised Algorithms for Microarray Sample Stratification.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Nextcast: A software suite to analyse and model toxicogenomics data.Computational and structural biotechnology journal · 2022Article
- Supervised Methods for Biomarker Detection from Microarray Experiments.Methods in molecular biology (Clifton, N.J.) · 2022Article
- MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification.Nature communications · 2021Article
- Vertical integration methods for gene expression data analysis.Briefings in bioinformatics · 2021Review
- Integrating multidimensional data for clustering analysis with applications to cancer patient data.Journal of the American Statistical Association · 2021Article
- Integration of Proteomics and Other Omics Data.Methods in molecular biology (Clifton, N.J.) · 2021Article
- Data integration by fuzzy similarity-based hierarchical clustering.BMC bioinformatics · 2020Article
- Multi-view clustering for multi-omics data using unified embedding.Scientific reports · 2020Article
- PathME: pathway based multi-modal sparse autoencoders for clustering of patient-level multi-omics data.BMC bioinformatics · 2020Article
- Transcriptomics in Toxicogenomics, Part III: Data Modelling for Risk Assessment.Nanomaterials (Basel, Switzerland) · 2020Review
- Graph- and rule-based learning algorithms: a comprehensive review of their applications for cancer type classification and prognosis using genomic data.Briefings in bioinformatics · 2020Review
- Multi-parametric and multi-regional histogram analysis of MRI: modality integration reveals imaging phenotypes of glioblastoma.European radiology · 2019Article
- A multiobjective multi-view cluster ensemble technique: Application in patient subclassification.PloS one · 2019Article
- Data integration by multi-tuning parameter elastic net regression.BMC bioinformatics · 2018Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMultiple high-throughput molecular profiling by omics technologies can be collected for the same individuals. Combining these data, rather than exploiting them separately, can significantly increase the power of clinically relevant patients subclassifications.
resultsWe propose a multi-view approach in which the information from different data layers (views) is integrated at the levels of the results of each single view clustering iterations. It works by factorizing the membership matrices in a late integration manner. We evaluated the effectiveness and the performance of our method on six multi-view cancer datasets. In all the cases, we found patient sub-classes with statistical significance, identifying novel sub-groups previously not emphasized in literature. Our method performed better as compared to other multi-view clustering algorithms and, unlike other existing methods, it is able to quantify the contribution of single views on the final results.
conclusionOur observations suggest that integration of prior information with genomic features in the subtyping analysis is an effective strategy in identifying disease subgroups. The methodology is implemented in R and the source code is available online at http://neuronelab.unisa.it/a-multi-view-genomic-data-integration-methodology/ .
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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.