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  "Title": "A Biomarker Validation Approach for Classification and\nPredicting Survival Using Metabolomics Signature",
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  "Description": "An approach to identifies metabolic biomarker signature\nfor metabolic data by discovering predictive metabolite for\npredicting survival and classifying patients into risk groups.\nClassifiers are constructed as a linear combination of\npredictive/important metabolites, prognostic factors and\ntreatment effects if necessary. Several methods were\nimplemented to reduce the metabolomics matrix such as the\nprinciple component analysis of Wold Svante et al. (1987)\n<doi:10.1016/0169-7439(87)80084-9> , the LASSO method by Robert\nTibshirani (1998)\n<doi:10.1002/(SICI)1097-0258(19970228)16:4%3C385::AID-SIM380%3E3.0.CO;2-3>,\nthe elastic net approach by Hui Zou and Trevor Hastie (2005)\n<doi:10.1111/j.1467-9868.2005.00503.x>. Sensitivity analysis on\nthe quantile used for the classification can also be accessed\nto check the deviation of the classification group based on the\nquantile specified. Large scale cross validation can be\nperformed in order to investigate the mostly selected\npredictive metabolites and for internal validation. During the\nevaluation process, validation is accessed using the hazard\nratios (HR) distribution of the test set and inference is\nmainly based on resampling and permutations technique.",
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