Discovery of pharmaceutically-targetable pathways and prediction of survivorship for pneumonia and sepsis patients from the view point of ensemble gene noise

Tristan de Jong; Victor Guryev; Yury M. Moshkin

To predict the mortality of CAP and sepsis patients we trained gradient boosted regression tree models with a scalable tree boosting system XGBoost [36] using mortality within 28 days as a binary response variable, and ensemble gene noise and age as independent model features. To this end, we split individuals into discovery and validation cohorts following exactly the same partitioning as annotated in GSE65682 [8] . Then, we trained 3 models: 1) a model predicting mortality for CAP and sepsis patients, 2) a model predicting mortality for CAP patients, and 3) a model predicting mortality for sepsis patients. Models features were preselected using discovery cohorts by t test comparing ensembles gene noise for survived and deceased patients . CC-BY-NC 4.0 International license author/funder. It is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.04.10.035717 doi: bioRxiv preprint to maximize the accuracy of XGBoost training on the discovery data sets. For CAP and sepsis (1) , and sepsis (3) models, the cut-off for the model features was set at p ≤ 0.01, and for the CAP model (2) -at p ≤ 0.05. The XGBoost hyper tuning parameters (learning rate (), complexity (), depth, etc.) were optimized by cross validation. To avoid overfitting, we found early epoch stopping parameters by randomly splitting of the discovery cohort into two equal folds: training and test. Then, the validation cohorts, which were hidden from feature selection and model training, were used to verify the accuracy of the final models. The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.04.10.035717 doi: bioRxiv preprint Mitochondrial disfunction resulting in reduced respiratory chain complex I activity and low ATP levels is a whole mark for sepsis. [30] KEGG: Osteoclast differentiation Mean expression of osteoclast differentiation genes is up-regulated in human septic shock. [66] KEGG: Tight junction Sepsis disrupts intestinal barrier which leads to a multiple organ dysfunction syndrome and alters the expression of tight junction proteins.