Yang Li, PhD, MS
Associate Research Scientist in Cell BiologyDownloadHi-Res Photo
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Appointments
Cell Biology
Primary
Contact Info
About
Titles
Associate Research Scientist in Cell Biology
Appointments
Cell Biology
Associate Research ScientistPrimary
Other Departments & Organizations
Education & Training
- PhD
- Clemson University, Bioengineering (2020)
- Graduate Research Assistant
- Clemson University (2020)
- MS
- Tianjin University, Optical Engineering (2015)
Research
Overview
Medical Research Interests
Flavin-Adenine Dinucleotide; Flavins; Myocytes, Cardiac; NAD; NADP; Osteochondritis; Spheroids, Cellular
ORCID
0000-0002-2413-7875- View Lab Website
Bewersdorf Lab
Research at a Glance
Publications Timeline
A big-picture view of Yang Li's research output by year.
10Publications
14Citations
Publications
2024
On Optimality of Mallows Model Averaging
Peng J, Li Y, Yang Y. On Optimality of Mallows Model Averaging. Journal Of The American Statistical Association 2024, ahead-of-print: 1-12. DOI: 10.1080/01621459.2024.2402566.Peer-Reviewed Original ResearchAltmetricConceptsNon-nested setAsymptotic optimalityModel selectionCp criterionMallows model averageCandidate modelsOptimal convex combinationMallows' Cp criterionMinimax adaptiveSampling inequalitiesMallows modelConvex combinationTheoretical findingsOptimal riskSupplementary materialsMA estimatesModel AveragingTheoretical justificationModel weightsAverage modelNested setMild conditionsMinimaxEstimationInequalityIntegrative deep learning with prior assisted feature selection
Wang F, Jia K, Li Y. Integrative deep learning with prior assisted feature selection. Statistics In Medicine 2024, 43: 3792-3814. PMID: 38923006, DOI: 10.1002/sim.10148.Peer-Reviewed Original ResearchConceptsDeep learningLearning methodsPerformance of feature selectionCapability of deep learningDeep learning methodsEnsemble learning methodFeature selectionPrior informationLearningIntegrated analysisSkin cutaneous melanomaDatasetRedundancyMethodSimulation studySuperioritySelectionCapabilityInformationFrameworkSequential covariate-adjusted randomization via hierarchically minimizing Mahalanobis distance and marginal imbalance
Yang H, Qin Y, Li Y, Hu F. Sequential covariate-adjusted randomization via hierarchically minimizing Mahalanobis distance and marginal imbalance. Biometrics 2024, 80: ujae047. PMID: 38801258, DOI: 10.1093/biomtc/ujae047.Peer-Reviewed Original ResearchAltmetricMeSH Keywords and ConceptsConceptsCovariate balanceMarginal imbalanceAdaptive randomization methodsTreatment effect estimatesSequential allocation schemeModified Mahalanobis distanceRandomization methodSimulation studyCovariate imbalanceRandom processTheoretical guaranteesCovariatesImbalance measureEffect estimatesMahalanobis distanceCurrent patientSample size differencesMarginal balanceAllocation schemeConvergenceData analysisSuperior performance
2023
Modifiable risk factors for esophageal cancer in endoscopic screening population: A modeling study
Zhang Q, Wang F, Feng H, Xing J, Zhu S, Zhang H, Li Y, Wei W, Zhang S. Modifiable risk factors for esophageal cancer in endoscopic screening population: A modeling study. Chinese Medical Journal 2023, 137: 350-352. PMID: 38030561, PMCID: PMC10836889, DOI: 10.1097/cm9.0000000000002878.Peer-Reviewed Original ResearchZINBMM: a general mixture model for simultaneous clustering and gene selection using single-cell transcriptomic data
Li Y, Wu M, Ma S, Wu M. ZINBMM: a general mixture model for simultaneous clustering and gene selection using single-cell transcriptomic data. Genome Biology 2023, 24: 208. PMID: 37697330, PMCID: PMC10496184, DOI: 10.1186/s13059-023-03046-0.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsSingle-cell transcriptomic dataCell heterogeneitySingle-cell RNA sequencing data analysisRNA sequencing data analysisCluster-specific genesGene selectionScRNA-seq datasetsSequencing data analysisNegative binomial mixture modelTranscriptomic dataCell lineagesCell typesBinomial mixture modelsBiological understandingBatch effectsDropout eventsLineagesGenesRaw countsCritical componentSelectionSystemic analysisVisualization and assessment of model selection uncertainty
Qin Y, Wang L, Li Y, Li R. Visualization and assessment of model selection uncertainty. Computational Statistics & Data Analysis 2023, 178: 107598. DOI: 10.1016/j.csda.2022.107598.Peer-Reviewed Original ResearchCitationsAltmetric
2022
Spatio-temporally smoothed deep survival neural network
Li Y, Liang D, Ma S, Ma C. Spatio-temporally smoothed deep survival neural network. Journal Of Biomedical Informatics 2022, 137: 104255. PMID: 36462600, PMCID: PMC9845179, DOI: 10.1016/j.jbi.2022.104255.Peer-Reviewed Original ResearchCitationsNetwork-based cancer heterogeneity analysis incorporating multi-view of prior information
Li Y, Xu S, Ma S, Wu M. Network-based cancer heterogeneity analysis incorporating multi-view of prior information. Bioinformatics 2022, 38: 2855-2862. PMID: 35561185, PMCID: PMC9113254, DOI: 10.1093/bioinformatics/btac183.Peer-Reviewed Original ResearchCitations
2021
Confidence graphs for graphical model selection
Wang L, Qin Y, Li Y. Confidence graphs for graphical model selection. Statistics And Computing 2021, 31: 52. DOI: 10.1007/s11222-021-10027-5.Peer-Reviewed Original ResearchConceptsGraphical model selectionModel selection uncertaintyResidual bootstrap procedureTraditional confidence intervalsSelection uncertaintyModel selectionModel selection methodsBootstrap procedureTrue modelDependence structureSampling distributionGraphModel settingsGraphical modelsPopulation parametersSelection methodNumerical studyGraphical toolConfidence intervals
2014
Regularized receiver operating characteristic-based logistic regression for grouped variable selection with composite criterion
Li Y, Yu C, Qin Y, Wang L, Chen J, Yi D, Shia B, Ma S. Regularized receiver operating characteristic-based logistic regression for grouped variable selection with composite criterion. Journal Of Statistical Computation And Simulation 2014, 85: 2582-2595. DOI: 10.1080/00949655.2014.899362.Peer-Reviewed Original ResearchCitationsConcepts