Feature selection for kernel methods in systems biology.

ABSTRACT: The substantial development of high-throughput biotechnologies has rendered large-scale multi-omics datasets increasingly available. New challenges have emerged to process and integrate this large volume of information, often obtained from widely heterogeneous sources. Kernel methods have proven successful to handle the analysis of different types of datasets obtained on the same individuals. However, they usually suffer from a lack of interpretability since the original description of the individuals is lost due to the kernel embedding. We propose novel feature selection methods that are adapted to the kernel framework and go beyond the well-established work in supervised learning by addressing the more difficult tasks of unsupervised learning and kernel output learning. The method is exp

SUBMITTER: Brouard C 

PROVIDER: S-EPMC8900155 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature