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
Yale Co-Authors
Frequent collaborators of Yang Li's published research.
Publications Timeline
A big-picture view of Yang Li's research output by year.
Shuangge Steven Ma, PhD
15Publications
68Citations
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 conditionsMinimaxEstimationInequalityGroup Response‐Adaptive Randomization With Delayed and Missing Responses
Zhai G, Li Y, Zhang L, Hu F. Group Response‐Adaptive Randomization With Delayed and Missing Responses. Statistics In Medicine 2024, 43: 5047-5059. PMID: 39285137, DOI: 10.1002/sim.10220.Peer-Reviewed Original ResearchMeSH Keywords and ConceptsIncorporating prior information in gene expression network-based cancer heterogeneity analysis
Li R, Xu S, Li Y, Tang Z, Feng D, Cai J, Ma S. Incorporating prior information in gene expression network-based cancer heterogeneity analysis. Biostatistics 2024, kxae028. PMID: 39074174, DOI: 10.1093/biostatistics/kxae028.Peer-Reviewed Original ResearchIntegrative 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 analysisBalancing covariates in multi-arm trials via adaptive randomization
Yang H, Qin Y, Wang F, Li Y, Hu F. Balancing covariates in multi-arm trials via adaptive randomization. Computational Statistics & Data Analysis 2023, 179: 107642. DOI: 10.1016/j.csda.2022.107642.Peer-Reviewed Original ResearchCitationsVisualization 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 ResearchCitations