Supervised multiple kernel learning approaches for multi-omics data integration.

ABSTRACT:

Background

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.

Results

We 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

SUBMITTER: Briscik M 

PROVIDER: S-EPMC11585117 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature