Multi-omic modelling of inflammatory bowel disease with regularized canonical correlation analysis.

ABSTRACT:

Background

Personalized medicine requires finding relationships between variables that influence a patient's phenotype and predicting an outcome. Sparse generalized canonical correlation analysis identifies relationships between different groups of variables. This method requires establishing a model of the expected interaction between those variables. Describing these interactions is challenging when the relationship is unknown or when there is no pre-established hypothesis. Thus, our aim was to develop a method to find the relationships between microbiome and host transcriptome data and the relevant clinical variables in a complex disease, such as Crohn's disease.

Results

We present here a method to identify interactions based on canonical correlation analysis. We show tha

SUBMITTER: Revilla L 

PROVIDER: S-EPMC7870068 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature