Additional file 1 of A diagnostic model for sepsis-induced acute lung injury using a consensus machine learning approach and its therapeutic implications
Creators: name: Zheng, Yongxin
name: Wang, Jinping
name: Ling, Zhaoyi
name: Zhang, Jiamei
name: Zeng, Yuan
name: Wang, Ke
name: Zhang, Yu
name: Nong, Lingbo
name: Sang, Ling
name: Xu, Yonghao
name: Liu, Xiaoqing
name: Li, Yimin
name: Huang, Yongbo
Publisher: name: figshare
Year: 2023
Subjects: subject: Biochemistry
subject: Space Science
subject: Medicine
subject: Microbiology
subject: FOS: Biological sciences
subject: Cell Biology
subject: Genetics
subject: Molecular Biology
subject: Biological Sciences not elsewhere classified
subject: Information Systems not elsewhere classified
subject: Cancer
subject: Hematology

description: Additional file 1: Figure S1. The architecture of stacking model involves multiple base models (Elastic Net, svm, Random Forest and XGBoost), and a meta-model (stacking ensemble model) that combines the predictions in the base models. The meta-model is trained on the predictions made by base models on out-of-sample data. In this study, the training datasets were prepared for the meta-model is via 10-fold cross-validation of base models, where the out-of-fold predictions are used as the basis for the training dataset for the meta-model. Multiple base-models are often complex and diverse, using a diverse range of models (Elastic Net, svm, Random Forest and XGBoost) that make different assumptions about the prediction. The meta-model is often simple which provide a smooth interpretation of the predictions made by the base models. Thus, linear models were used as the meta-model, and the predictions made is a weighted average of the predictions made by the base models. Figure S2. Data processing. A, B PCA analysis and Box plot of expression profiles before batch effect correction. C, D PCA analysis and Box plot of expression profiles after batch effect correction. E t-SNE plot of subjects before batch correction with Combat. F t-SNE plot of subjects after batch correction with Combat. Figure S3. The functional analysis for sepsis-induced ALI. A Biological process. B Cell component. C Molecular function. D KEGG pathway enrichment analysis. Figure S4. Correlation plot of genes remaining after filtering out those with high correlation (Spearman’s &gt;0.5). Figure S5. The DEGs selected by each machine learning methods. A–D Elastic net, svm, random forest and XGBoost were conducted to select features and sort by importance. Figure S6. The expression levels of genes selected by at least two methods. A The expression of selected genes in sepsis and sepsis-induced ALI. B Heatmap showed the selected genes expression in sepsis and sepsis-induced ALI. Figure S7. Individual diagnostic genes CIC used to assess the performance of nomogram. A–D The CIC of ARHGDIB, ALDH1A1, TACR3, TREM1 and PI3. Figure S8. Potential mechanisms of diagnostic genes contributing to the development of ALI/ARDS. Table S1. Basic information of the datasets included in this study. Table S2. Tripod Checklist. Table S3: Parameters used to optimise an XGBoost classifier for sepsis-induced using genes. a the range of each parameter tuned, b the optimal parameter for the initial xgboost model, c the final parameter value used for an xgboost model trained on a reduced number of genes.