2017
Introduction to Bayesian variable selection methods in high-dimensional omics data analysis
Dong X, Xu S, Tao R, Wang T. Introduction to Bayesian variable selection methods in high-dimensional omics data analysis. Chinese Journal Of Epidemiology 2017, 38: 679-683. PMID: 28651411, DOI: 10.3760/cma.j.issn.0254-6450.2017.05.025.Peer-Reviewed Original ResearchConceptsBayesian variable selection methodOmics dataBayesian variable selectionAnalysis of high-dimensional dataDevelopment of genome sequencing technologyVariable selection methodsHigh-dimensional omics data analysisCase nGenome sequencing technologiesOmics data analysisVariable selectionVariable PHigh-dimensional dataSequencing technologiesStatistical challengesMeasure thousandsOmicsProgression of diseaseBioinformatics
2015
Down-regulation of PRKCB1 expression in Han Chinese patients with subsyndromal symptomatic depression
Guo X, Li Z, Zhang C, Yi Z, Li H, Cao L, Yuan C, Hong W, Wu Z, Peng D, Chen J, Xia W, Zhao G, Wang F, Yu S, Cui D, Xu Y, Golam CM, Smith AK, Wang T, Fang Y. Down-regulation of PRKCB1 expression in Han Chinese patients with subsyndromal symptomatic depression. Journal Of Psychiatric Research 2015, 69: 1-6. PMID: 26343587, DOI: 10.1016/j.jpsychires.2015.07.011.Peer-Reviewed Original ResearchConceptsSubsyndromal symptomatic depressionPeripheral blood mononuclear cellsHan Chinese patientsSSD patientsMRNA expression levelsMDD patientsSymptomatic depressionChinese patientsBlood mononuclear cellsSingle nucleotide polymorphismsExpression levelsMononuclear cellsPatientsMRNA expressionCommon diseaseReal-time quantitative PCR studiesSignificant decreaseSignificant social dysfunctionSocial dysfunctionPCR studiesCandidate genesTranscript networksFunctional annotation toolMultiple single nucleotide polymorphismsQuantitative PCR studies