Deep learning generates custom-made logistic regression models for explaining how breast cancer subtypes are classified.

ABSTRACT: Differentiating the intrinsic subtypes of breast cancer is crucial for deciding the best treatment strategy. Deep learning can predict the subtypes from genetic information more accurately than conventional statistical methods, but to date, deep learning has not been directly utilized to examine which genes are associated with which subtypes. To clarify the mechanisms embedded in the intrinsic subtypes, we developed an explainable deep learning model called a point-wise linear (PWL) model that generates a custom-made logistic regression for each patient. Logistic regression, which is familiar to both physicians and medical informatics researchers, allows us to analyze the importance of the feature variables, and the PWL model harnesses these practical abilities of logistic regression. In t

SUBMITTER: Shibahara T 

PROVIDER: S-EPMC10202302 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature