Supervised Multiple Kernel Learning approaches for multi-omics data
  integration
Mitja Briscik Gabriele Tazza Marie-Agnes Dillies László Vidács Sébastien Dejean
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.