ArticleBioData mining2024
Supervised multiple kernel learning approaches for multi-omics data integration.
Article in BioData mining, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Diagnosing Anaerobic Digesters' Function and Performance: From Meta-Omics to Integrated Meta-Omics Analyses.Environmental microbiology reports · 2026Review
- Integrative Analysis of Multimodal Omics Data.Annual review of statistics and its application · 2026Article
- Kernel-DMD for multiome data integration and control.PLoS computational biology · 2026Article
- Integrating Genomics, Radiomics, and Pathomics in Oncology: A Scoping Review and a Framework for AI-Enabled Surgomics.Bioengineering (Basel, Switzerland) · 2026Review
- Machine learning for extracellular vesicles enables diagnostic and therapeutic nanobiotechnology.Journal of nanobiotechnology · 2026Review
- A review of multi-omics integration techniques across five machine learning method families.Bioinformatics advances · 2026Review
- Bridging scales: integrated multi-omics and deep phenotyping for climate resilience in crop plants.Frontiers in plant science · 2026Review
- Decoding the genomic symphony: unravelling brain disorders through data integration and machine learning.Molecular psychiatry · 2025Review
- Genomic and hyperspectral imaging-based prediction blending enables selection for reduced deoxynivalenol content in wheat grains.G3 (Bethesda, Md.) · 2025Article
- Mechanotransduction-Epigenetic Coupling in Pulmonary Regeneration: Multifunctional Bioscaffolds as Emerging Tools.Pharmaceuticals (Basel, Switzerland) · 2025Review
- A hybrid method for fusion cardiac biomarkers and echocardiography videos in the experimental classification of Trypanosoma cruzi infection.Biomedical engineering online · 2025Article
- Bottlenecks in advancing and applying multiomic data integration-common data resources as rate-limiting drivers-the high-impact use case of atherosclerotic cardiovascular disease.Briefings in bioinformatics · 2025Review
- Interpretable and integrative analysis of single-cell multiomics with scMKL.Communications biology · 2025Article
- Individual-based modeling unravels spatial and social interactions in bacterial communities.The ISME journal · 2025Review
- MINN: A metabolic-informed neural network for integrating omics data into genome-scale metabolic modeling.Computational and structural biotechnology journal · 2025Article
- GAIN-BRCA: a graph-based AI-net framework for breast cancer subtype classification using multiomics data.Bioinformatics advances · 2025Article
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Authors and funding
5 authors.
Funding
Abstract
backgroundAdvances in high-throughput technologies have originated an ever-increasing availability of omics datasets. The integration of multiple heterogeneous data sources is currently an issue for biology and bioinformatics. Multiple kernel learning (MKL) has shown to be a flexible and valid approach to consider the diverse nature of multi-omics inputs, despite being an underused tool in genomic data mining.
resultsWe provide novel MKL approaches based on different kernel fusion strategies. To learn from the meta-kernel of input kernels, we adapted unsupervised integration algorithms for supervised tasks with support vector machines. We also tested deep learning architectures for kernel fusion and classification. The results show that MKL-based models can outperform more complex, state-of-the-art, supervised multi-omics integrative approaches.
conclusionMultiple kernel learning offers a natural framework for predictive models in multi-omics data. It proved to provide a fast and reliable solution that can compete with and outperform more complex architectures. Our results offer a direction for bio-data mining research, biomarker discovery and further development of methods for heterogeneous data integration.
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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.