ArticleBioinformatics (Oxford, England)2020
Multiple kernel learning for integrative consensus clustering of omic datasets.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Ensemble clustering: A practical tutorial.Behavior research methods · 2026Article
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- CSIE: cancer subtyping via inference and ensemble.Briefings in bioinformatics · 2026Article
- CEDR: robust consensus cancer subtyping with multi-omics data via ensemble dimensionality reduction.Briefings in bioinformatics · 2026Article
- A review of multi-omics integration techniques across five machine learning method families.Bioinformatics advances · 2026Review
- Article
- Learning to estimate sample-specific transcriptional networks for 7,000 tumors.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- VICatMix: variational Bayesian clustering and variable selection for discrete biomedical data.Bioinformatics advances · 2025Article
- Identify characteristics of Vietnamese oral squamous cell carcinoma patients by machine learning on transcriptome and clinical-histopathological analysis.Journal of dental sciences · 2024Article
- scMNMF: a novel method for single-cell multi-omics clustering based on matrix factorization.Briefings in bioinformatics · 2024Article
- intCC: An efficient weighted integrative consensus clustering of multimodal data.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2024Article
- COMMO: a web server for the identification and analysis of consensus gene modules across multiple methods.Bioinformatics (Oxford, England) · 2023Article
- Multi-view clustering by CPS-merge analysis with application to multimodal single-cell data.PLoS computational biology · 2023Article
- Genomic Patterns of Malignant Peripheral Nerve Sheath Tumor (MPNST) Evolution Correlate with Clinical Outcome and Are Detectable in Cell-Free DNA.Cancer discovery · 2023Article
- A toolbox of machine learning software to support microbiome analysis.Frontiers in microbiology · 2023Review
- Combining Molecular, Imaging, and Clinical Data Analysis for Predicting Cancer Prognosis.Cancers · 2022Review
- Consensus clustering applied to multi-omics disease subtyping.BMC bioinformatics · 2021Article
- Computational strategies for single-cell multi-omics integration.Computational and structural biotechnology journal · 2021Review
- GPseudoClust: deconvolution of shared pseudo-profiles at single-cell resolution.Bioinformatics (Oxford, England) · 2020Article
- Looking beyond the hype: Applied AI and machine learning in translational medicine.EBioMedicine · 2019Review
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2 authors.
Funding
Abstract
motivationDiverse applications-particularly in tumour subtyping-have demonstrated the importance of integrative clustering techniques for combining information from multiple data sources. Cluster Of Clusters Analysis (COCA) is one such approach that has been widely applied in the context of tumour subtyping. However, the properties of COCA have never been systematically explored, and its robustness to the inclusion of noisy datasets is unclear.
resultsWe rigorously benchmark COCA, and present Kernel Learning Integrative Clustering (KLIC) as an alternative strategy. KLIC frames the challenge of combining clustering structures as a multiple kernel learning problem, in which different datasets each provide a weighted contribution to the final clustering. This allows the contribution of noisy datasets to be down-weighted relative to more informative datasets. We compare the performances of KLIC and COCA in a variety of situations through simulation studies. We also present the output of KLIC and COCA in real data applications to cancer subtyping and transcriptional module discovery. AVAILABILITY AND IMPLEMENTATION: R packages klic and coca are available on the Comprehensive R Archive Network. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.