We used LASSO logistic regression to identify the most informative features for modeling the somatic mutation rate in GC. As it is computationally expensive to run a logistic regression on all positions in the non-coding genome with a large number of predictor variables, we used all mutated sites and an equal number of randomly sampled non-mutated sites as the input for feature selection in the LASSO logistic regression model. We regressed the binary mutation status of each site against the mean signal of each feature over an 11 bp region centered at the site. The regularization parameter λ was chosen by 10-fold cross-validation such that the error of the selected model was within one standard deviation from the minimum error. LASSO regression and cross validation were performed using the “glmnet” package in R.\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{glmnet}(y\sim \beta X,\mathrm{family} = \mathrm{logistic})$$\end{document}glmnet(y~βX,family=logistic)