Interpretable genotype-to-phenotype classifiers with performance guarantees.

ABSTRACT: Understanding the relationship between the genome of a cell and its phenotype is a central problem in precision medicine. Nonetheless, genotype-to-phenotype prediction comes with great challenges for machine learning algorithms that limit their use in this setting. The high dimensionality of the data tends to hinder generalization and challenges the scalability of most learning algorithms. Additionally, most algorithms produce models that are complex and difficult to interpret. We alleviate these limitations by proposing strong performance guarantees, based on sample compression theory, for rule-based learning algorithms that produce highly interpretable models. We show that these guarantees can be leveraged to accelerate learning and improve model interpretability. Our approach is validat

SUBMITTER: Drouin A 

PROVIDER: S-EPMC6411721 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature