Mika Kigawa, Akiko Tsuchida, Kenta Matsumura, Mika Ito, Tomomi Tanaka, Kei Hamazaki, Yuichi Adachi, Hidekuni Inadera
Differences in health literacy related to gestational weight gain and children's birth weight according to maternal nativity status in the Japan Environment and Children's Study (JECS): a longitudinal cohort study
BMJ open
survival prediction using highdimensional molecular data hot topic field genomics precision medicine especially cancer studies considering carcinogenesis pathwaybased pathogenesis developing models using group structures closer mimic disease progression prognosis many approaches used integrate group information however singlemodel methods may account unstable prediction introduced novel survival stacking method modeled using group structure information improve robustness cancer survival prediction context highdimensional omics data super learner survival stacking combines prediction multiple submodels independently trained using features pregrouped biological pathways addition nonnegative linear combination submodels extended super learner nonnegative bayesian hierarchical generalized linear model artificial neural network compared proposed modeling strategy widely used survival penalized method lasso cox several group penalized methods eg group lasso cox via simulation study realworld data application proposed survival stacking method showed superior robust performance terms discrimination compared singlemodel methods case highnoise simulated data realworld data nonnegative bayesian stacking method identify important biological signal pathways genes associated prognosis cancer study proposed novel survival stacking strategy incorporating biological group information cancer prognosis models additionally study extended super learner nonnegative bayesian model ann enriching combination submodels proposed bayesian stacking strategy exhibited favorable properties prediction interpretation complex survival data may aid discovering cancer targets