Featured Publications
Detecting differentially expressed genes by relative entropy
Yan X, Deng M, Fung K, Qian M. Detecting differentially expressed genes by relative entropy. Journal Of Theoretical Biology 2005, 234: 395-402. PMID: 15784273, DOI: 10.1016/j.jtbi.2004.11.039.Peer-Reviewed Original Research
2017
Validation of a 52-gene risk profile for outcome prediction in patients with idiopathic pulmonary fibrosis: an international, multicentre, cohort study
Herazo-Maya JD, Sun J, Molyneaux PL, Li Q, Villalba JA, Tzouvelekis A, Lynn H, Juan-Guardela BM, Risquez C, Osorio JC, Yan X, Michel G, Aurelien N, Lindell KO, Klesen MJ, Moffatt MF, Cookson WO, Zhang Y, Garcia JGN, Noth I, Prasse A, Bar-Joseph Z, Gibson KF, Zhao H, Herzog EL, Rosas IO, Maher TM, Kaminski N. Validation of a 52-gene risk profile for outcome prediction in patients with idiopathic pulmonary fibrosis: an international, multicentre, cohort study. The Lancet Respiratory Medicine 2017, 5: 857-868. PMID: 28942086, PMCID: PMC5677538, DOI: 10.1016/s2213-2600(17)30349-1.Peer-Reviewed Original ResearchMeSH KeywordsAgedCohort StudiesFemaleGene Expression ProfilingGenetic MarkersGenetic TestingHumansIdiopathic Pulmonary FibrosisLeukocytes, MononuclearLinear ModelsMaleMiddle AgedOligonucleotide Array Sequence AnalysisPrognosisProportional Hazards ModelsRisk AssessmentRisk FactorsTime FactorsVital CapacityConceptsIdiopathic pulmonary fibrosisTransplant-free survivalRisk profilePulmonary fibrosisAntifibrotic drugsPeripheral blood mononuclear cellsCox proportional hazards modelClinical prediction toolGroup of patientsBlood mononuclear cellsHigh-risk groupProportional hazards modelPulmonary Fibrosis FoundationPittsburgh cohortUntreated patientsCohort studyClinical courseIPF diagnosisBlood InstituteProspective studyVital capacityMononuclear cellsPeripheral bloodUS National InstitutesNational Heart
2008
Nonlinear cooperation of p53-ING1-induced bax expression and protein S-nitrosylation in GSNO-induced thymocyte apoptosis: a quantitative approach with cross-platform validation
Duan S, Wan L, Fu WJ, Pan H, Ding Q, Chen C, Han P, Zhu X, Du L, Liu H, Chen Y, Liu X, Yan X, Deng M, Qian M. Nonlinear cooperation of p53-ING1-induced bax expression and protein S-nitrosylation in GSNO-induced thymocyte apoptosis: a quantitative approach with cross-platform validation. Apoptosis 2008, 14: 236. PMID: 19082896, DOI: 10.1007/s10495-008-0288-4.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsAnimalsApoptosisBcl-2-Associated X ProteinDexamethasoneGene DosageGene Expression RegulationInhibitor of Growth Protein 1Intracellular Signaling Peptides and ProteinsMiceModels, BiologicalNeural Networks, ComputerNonlinear DynamicsNuclear ProteinsOligonucleotide Array Sequence AnalysisProtein BindingReproducibility of ResultsReverse Transcriptase Polymerase Chain ReactionS-NitrosoglutathioneThymus GlandTumor Suppressor Protein p53Tumor Suppressor Proteins
2007
Inferring activity changes of transcription factors by binding association with sorted expression profiles
Cheng C, Yan X, Sun F, Li LM. Inferring activity changes of transcription factors by binding association with sorted expression profiles. BMC Bioinformatics 2007, 8: 452. PMID: 18021409, PMCID: PMC2194743, DOI: 10.1186/1471-2105-8-452.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsBase SequenceBinding SitesGene Expression ProfilingMolecular Sequence DataOligonucleotide Array Sequence AnalysisProtein BindingSequence AlignmentSequence Analysis, DNAStructure-Activity RelationshipTranscription FactorsConceptsTranscription factorsExpression profilesMicroarray dataTarget gene selectionPost-transcriptional modificationsChIP-chip dataMicroarray expression profilesExpression differentiationLow expression levelsProfile of expressionTarget genesRegulatory mechanismsGene expressionBiological processesMicroarray studiesAffinity dataGene selectionSame machineryExpression levelsGenesActivity changesSignificance cutoffDifferentiationMeaningful hypothesesAffinity scores
2006
MARD: a new method to detect differential gene expression in treatment-control time courses
Cheng C, Ma X, Yan X, Sun F, Li LM. MARD: a new method to detect differential gene expression in treatment-control time courses. Bioinformatics 2006, 22: 2650-2657. PMID: 16928738, DOI: 10.1093/bioinformatics/btl451.Peer-Reviewed Original Research