Jian Zhang, Fenhua Zhou, Jinxia Jiang, Xia Duan, Xin Yang
Effective Teaching Behaviors of Clinical Nursing Teachers: A Qualitative Meta-Synthesis
Frontiers in public health
study aims identify unique metabolomics biomarkers associated type 2 diabetes t2d develop accurate diagnostics model using treebased machine learning ml algorithms integrated bioinformatics techniques univariate multivariate analyses fold change receiver operating characteristic curve roc partial leastsquares discriminant analysis plsda used identify biomarker metabolites showed significant concentration t2d patients three treebased algorithms extreme gradient boosting xgboost light gradient boosting machine lightgbm adaptive boosting adaboost demonstrated robustness highdimensional data analysis used create diagnostic model t2d result biomarker discovery process validated three different approaches pyruvate drhamnose amp pipecolate tetradecenoic acid tetradecanoic acid dodecanediothioic acid prostaglandin e3d3 isobars adp hexadecenoic acid determined potential biomarkers t2d results showed xgboost model accuracy 0831 f1score 0845 sensitivity 0882 specificity 0774 positive predictive value ppv 0811 negativepv npv 0857 area roc curve auc 0887 slight highest performance measures ml integrated bioinformatics techniques offers accurate positive t2d candidate biomarker discovery xgboost model successfully distinguish t2d based metabolites