Fast and interpretable genomic data analysis using multiple approximate kernel learning.

ABSTRACT:

Motivation

Dataset sizes in computational biology have been increased drastically with the help of improved data collection tools and increasing size of patient cohorts. Previous kernel-based machine learning algorithms proposed for increased interpretability started to fail with large sample sizes, owing to their lack of scalability. To overcome this problem, we proposed a fast and efficient multiple kernel learning (MKL) algorithm to be particularly used with large-scale data that integrates kernel approximation and group Lasso formulations into a conjoint model. Our method extracts significant and meaningful information from the genomic data while conjointly learning a model for out-of-sample prediction. It is scalable with increasing sample size by approximating instead of calc

SUBMITTER: Bektas AB 

PROVIDER: S-EPMC9235505 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature