Supervised Multiple Kernel Learning approaches for multi-omics data integration

arXiv:2403.18355v1 Announce Type: cross
Abstract: 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 …

abstract advances arxiv availability bioinformatics biology cs.lg data data integration datasets data sources diverse integration issue kernel multiple nature stat.ap stat.ml technologies type