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Deep Learning Generates Custom-made Logistic Regression Models for Explaining How Breast Cancer Subtypes Are Classified by Shibahara, Takuma; Wada, Chisa; Yamashita, Yasuho; Fujita, Kazuhiro; Sato, Masamichi; Kuwata, Junichi; Okamoto, Atsushi; Ono, Yoshimasa is a scholarly article available to read on EtoBox.

What is Deep Learning Generates Custom-made Logistic Regression Models for Explaining How Breast Cancer Subtypes Are Classified about?

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 this study, we show that analyzing breast cancer subtypes is clinically beneficial for patients and one of the best ways to validate the capability of the PWL model. First, we trained the PWL model with RNA-seq data to predict PAM50 intrinsic subtypes and applied it to the 41/50 genes of PAM50 through the subtype prediction task. Second, we developed a deep enrichment analysis method to reveal the

Author

Shibahara, Takuma; Wada, Chisa; Yamashita, Yasuho; Fujita, Kazuhiro; Sato, Masamichi; Kuwata, Junichi; Okamoto, Atsushi; Ono, Yoshimasa

Published

2020

Language

EN