Advances 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.We provide novel MKL approaches based on different kernel fusion strategies.To learn from the meta-kernel of input kernels, we adaptedunsupervised 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 compete with more complex, state-of-the-art, supervised multi-omics integrative approaches. Multiple kernel learning offers a natural framework for predictive models in multi-omics genomic data. Our results offer a direction for bio-data mining research and further development of methods for heterogeneous data integration.
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