Unsupervised multiple kernel learning for heterogeneous data integration.
Journal:
Bioinformatics (Oxford, England)
Published Date:
Mar 15, 2018
Abstract
MOTIVATION: Recent high-throughput sequencing advances have expanded the breadth of available omics datasets and the integrated analysis of multiple datasets obtained on the same samples has allowed to gain important insights in a wide range of applications. However, the integration of various sources of information remains a challenge for systems biology since produced datasets are often of heterogeneous types, with the need of developing generic methods to take their different specificities into account.