Open AccessIncorporating prior knowledge into regularized regression
Author(s) -
Chubing Zeng,
Duncan C. Thomas,
Juan Pablo Lewinger
Publication year - 2020
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btaa776
Subject(s) - computer science , lasso (programming language) , bayes' theorem , feature selection , regression , feature (linguistics) , a priori and a posteriori , data mining , machine learning , linear regression , regression analysis , artificial intelligence , statistics , mathematics , bayesian probability , linguistics , philosophy , epistemology , world wide web
Associated with genomic features like gene expression, methylation and genotypes, used in statistical modeling of health outcomes, there is a rich set of meta-features like functional annotations, pathway information and knowledge from previous studies, that can be used post hoc to facilitate the interpretation of a model. However, using this meta-feature information a priori rather than post hoc can yield improved prediction performance as well as enhanced model interpretation.