2023
TheMarker: a comprehensive database of therapeutic biomarkers
Zhang Y, Zhou Y, Zhou Y, Yu X, Shen X, Hong Y, Zhang Y, Wang S, Mou M, Zhang J, Tao L, Gao J, Qiu Y, Chen Y, Zhu F. TheMarker: a comprehensive database of therapeutic biomarkers. Nucleic Acids Research 2023, 52: d1450-d1464. PMID: 37850638, PMCID: PMC10767989, DOI: 10.1093/nar/gkad862.Peer-Reviewed Original ResearchConceptsClinical practiceTherapeutic biomarkersTherapy-induced toxicityClinical outcomesPredictive biomarkersPharmacodynamic biomarkersClinical managementPrognostic biomarkerSafety biomarkersClinical developmentPatient statusPharmacological effectsMonitoring biomarkerTherapyBiomarkersAnticancer therapyDrug developmentDrug discoveryDisease classesStatusDatabase
2022
Machine learning prediction of exposure to acrylamide based on modelling of association between dietary exposure and internal biomarkers
Wan X, Zhang Y, Gao S, Shen X, Jia W, Pan X, Zhuang P, Jiao J, Zhang Y. Machine learning prediction of exposure to acrylamide based on modelling of association between dietary exposure and internal biomarkers. Food And Chemical Toxicology 2022, 170: 113498. PMID: 36328216, DOI: 10.1016/j.fct.2022.113498.Peer-Reviewed Original ResearchMeSH KeywordsAcetylcysteineAcrylamideAgedBiomarkersDietary ExposureHumansMachine LearningMiddle AgedConceptsDietary exposureElderly populationInternal exposureTotal energy intakeDietary acrylamide exposureChinese elderly populationAverage dietary intakeN-acetylExposure assessmentRegression modelsUrinary biomarkersDietary intakeUrinary contentAcrylamide exposureChinese cohortPhysical activityAccurate exposure assessmentEnergy intakeElderly participantsPotential health risksL-cysteineImportant covariatesLinear regression modelsHealth risksExposure