Summary of Supervised Multiple Kernel Learning Approaches For Multi-omics Data Integration, by Mitja Briscik et al.
Supervised Multiple Kernel Learning approaches for multi-omics data integration
by Mitja Briscik, Gabriele Tazza, Marie-Agnes Dillies, László Vidács, Sébastien Dejean
First submitted to arxiv on: 27 Mar 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Applications (stat.AP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to integrating multiple heterogeneous omics datasets is presented in this paper, which leverages multiple kernel learning (MKL) to combine diverse inputs. The authors demonstrate the effectiveness of MKL-based models in outperforming state-of-the-art supervised multi-omics integrative approaches on support vector machines and deep learning architectures. Specifically, they propose novel MKL approaches based on different kernel fusion strategies, adapting unsupervised integration algorithms for supervised tasks and testing deep learning architectures for kernel fusion and classification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MKL is a powerful tool that can be used to integrate multiple omics datasets, providing a natural framework for predictive models in multi-omics data. The results show that MKL-based models can outperform more complex, state-of-the-art supervised multi-omics integrative approaches. This paper offers a direction for bio-data mining research, biomarker discovery and further development of methods for heterogeneous data integration. |
Keywords
* Artificial intelligence * Classification * Deep learning * Supervised * Unsupervised
