Shuangge Steven Ma, PhD
Department Chair and Professor of BiostatisticsDownloadHi-Res Photo
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Additional Titles
Affiliated Faculty, Yale Institute for Global Health
Director, Biostatistics and Bioinformatics Shared Resource
Contact Info
Biostatistics
PO Box 208034, 60 College Street
New Haven, CT 06520-8034
United States
About
Titles
Department Chair and Professor of Biostatistics
Affiliated Faculty, Yale Institute for Global Health; Director, Biostatistics and Bioinformatics Shared Resource
Biography
Dr. Ma received his Ph.D. degree in statistics at University of Wisconsin in 2004. Prior to arriving at Yale, Dr. Ma was a Senior Fellow in Collaborative Health Studies Coordinating Center (CHSCC) and Department of Biostatistics at University of Washington. He has been involved in developing novel statistical and bioinformatics methodologies for analysis of cancer (NHL, breast cancer, melanoma, lung cancer), mental disorders, and cardiovascular diseases. He has also been involved in health economics research, with special interest in health insurance in developing countries.
Appointments
Biostatistics
ChairDualBiostatistics
ProfessorPrimary
Other Departments & Organizations
- Biostatistics
- Biostatistics
- Cancer Prevention and Control
- Center for Biomedical Data Science
- Center for Infection and Immunity
- Computational Biology and Biomedical Informatics
- Ma Lab
- SPORE in Lung Cancer
- SPORE in Skin Cancer
- Yale Cancer Center
- Yale Combined Program in the Biological and Biomedical Sciences (BBS)
- Yale Institute for Global Health
- Yale School of Public Health
- Yale School of Public Health - NEW
- Yale Ventures
- YSPH Global Health Concentration
Education & Training
- Postdoctoral Associate
- University of Washington (2006)
- PhD
- University of Wisconsin (2004)
- MS
- University of California at Los Angeles (2000)
Research
Overview
Develop novel statistical methodologies for complex data;
Study epidemiology and pathogenesis of multiple cancers, including breast cancer, NHL, melanoma and lung cancer;
Conduct survey studies in mainland China and Taiwan, investigating health insurance utilization and impact;
Provide statistical support to multiple biomedical studies.
Medical Research Interests
Biostatistics; Economics; Neoplasms
Public Health Interests
Cardiovascular Diseases; Mental Health; Health Policy; Genetics, Genomics, Epigenetics; Bioinformatics; Biomarkers; Cancer
Research at a Glance
Yale Co-Authors
Frequent collaborators of Shuangge Steven Ma's published research.
Publications Timeline
A big-picture view of Shuangge Steven Ma's research output by year.
Research Interests
Research topics Shuangge Steven Ma is interested in exploring.
Tassos C. Kyriakides, PhD
Caroline Helen Johnson, PhD
Sajid A Khan, MD, FACS, FSSO
Xinyi Shen, MPH
Yang Li, PhD, MS
Zhangsheng Yu
33Publications
62Citations
Neoplasms
Publications
2024
Local Clustering for Functional Data
Chen Y, Zhang Q, Ma S. Local Clustering for Functional Data. Journal Of Computational And Graphical Statistics 2024, 1-37. DOI: 10.1080/10618600.2024.2431057.Peer-Reviewed Original ResearchConceptsThe spike‐and‐slab quantile LASSO for robust variable selection in cancer genomics studies
Liu Y, Ren J, Ma S, Wu C. The spike‐and‐slab quantile LASSO for robust variable selection in cancer genomics studies. Statistics In Medicine 2024, 43: 4928-4983. PMID: 39260448, DOI: 10.1002/sim.10196.Peer-Reviewed Original ResearchCitationsConceptsAsymmetric Laplace distributionSpike-and-slab LASSORobust variable selection methodHeavy-tailed errorsRobust variable selectionHeavy-tailed distributionsAnalysis of high-dimensional genomic dataHigh-dimensional genomic dataExpectation-maximizationComprehensive simulation studyVariable selection methodsLaplace distributionCoordinate descent frameworkPosterior modeCancer genomics studiesRobust likelihoodVariable selectionSparsity patternSimulation studyComputational advantagesQuantile regressionNonrobust oneSelf-adaptationLoss functionGenomic studiesHigh-Dimensional Gene–Environment Interaction Analysis
Wu M, Li Y, Ma S. High-Dimensional Gene–Environment Interaction Analysis. Annual Review Of Statistics And Its Application 2024 DOI: 10.1146/annurev-statistics-112723-034315.Peer-Reviewed Original ResearchConceptsFixed- and random-effects analysisG-E interaction analysisG-E interactionsVariable selectionFrequentist analysisGene-environmentRandom effects analysisGeneral frameworkStatistical propertiesProgression of complex diseasesDimension reductionHypothesis testingG-EComplex diseasesGenetic factorsInteraction analysisNonlinear effect analysisStatistical perspectiveDisease outcomeEnvironmental factorsPrediction-basedEstimation-basedIncorporating 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 ResearchCTHRC1+ fibroblasts and SPP1+ macrophages synergistically contribute to pro-tumorigenic tumor microenvironment in pancreatic ductal adenocarcinoma
Li E, Cheung H, Ma S. CTHRC1+ fibroblasts and SPP1+ macrophages synergistically contribute to pro-tumorigenic tumor microenvironment in pancreatic ductal adenocarcinoma. Scientific Reports 2024, 14: 17412. PMID: 39075108, PMCID: PMC11286765, DOI: 10.1038/s41598-024-68109-z.Peer-Reviewed Original ResearchMeSH Keywords and ConceptsConceptsPancreatic ductal adenocarcinomaTumor-associated macrophagesTumor microenvironmentEpithelial mesenchymal transitionDuctal adenocarcinomaImmune-suppressive tumor microenvironmentPro-tumorigenic tumor microenvironmentPancreatic cancer casesHeterogeneous tumor microenvironmentCombination of single-cellCancer-associated myofibroblastsSurgical resectionMyeloid cellsCurrent therapiesCancer casesLethal cancersSurvival rateExtracellular matrixTreat cancerMesenchymal transitionTherapeutic targetAdenocarcinomaCellular populationsCancerIntercellular interactionsEditorial
Ma S. Editorial. Briefings In Bioinformatics 2024, 25: bbae453. PMID: 39288229, PMCID: PMC11407437, DOI: 10.1093/bib/bbae453.Peer-Reviewed Original ResearchHEARTSVG: a fast and accurate method for identifying spatially variable genes in large-scale spatial transcriptomics
Yuan X, Ma Y, Gao R, Cui S, Wang Y, Fa B, Ma S, Wei T, Ma S, Yu Z. HEARTSVG: a fast and accurate method for identifying spatially variable genes in large-scale spatial transcriptomics. Nature Communications 2024, 15: 5700. PMID: 38972896, PMCID: PMC11228050, DOI: 10.1038/s41467-024-49846-1.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsSpatially variable genesVariable genesSpatial expression patternsSpatial transcriptomics technologiesSpatial transcriptomics researchTranscriptome researchTranscriptomic technologiesBiological functionsExpression patternsSpatial transcriptomicsGenesState-of-the-art methodsColorectal cancer dataA penalized integrative deep neural network for variable selection among multiple omics datasets
Li Y, Ren X, Yu H, Sun T, Ma S. A penalized integrative deep neural network for variable selection among multiple omics datasets. Quantitative Biology 2024, 12: 313-323. DOI: 10.1002/qub2.51.Peer-Reviewed Original ResearchConceptsOmics data analysisAvailability of omics dataMultiple omics datasetsGene expression datasetsAggregate multiple datasetsDeep neural networksOmics dataIntegrated deep neural networkOmics datasetsExpression datasetsMultiple datasetsDeep learningDiverse originsNeural networkOmicsAbstract Deep learningVariable selection resultsSample sizeVariable selectionIntegrated analysis frameworkCognitive statusOvarian cancer patientsModel interpretationExtensive simulation studyDatasetPartial Hepatectomy and Ablation for Survival of Early-Stage Hepatocellular Carcinoma Patients: A Bayesian Emulation Analysis
Wang J, Im Y, Wang R, Ma S. Partial Hepatectomy and Ablation for Survival of Early-Stage Hepatocellular Carcinoma Patients: A Bayesian Emulation Analysis. Life 2024, 14: 661. PMID: 38929645, PMCID: PMC11204969, DOI: 10.3390/life14060661.Peer-Reviewed Original ResearchAltmetricConceptsOverall survivalHepatocellular carcinomaPartial hepatectomyTumor sizeAssociated with inferior overall survivalEarly-stage hepatocellular carcinomaEarly-stage HCC patientsInferior overall survivalHepatocellular carcinoma patientsAblation armCarcinoma patientsAblation therapyNo significant differenceTreatment regimensHCC patientsEmulated target trialSurgical proceduresEffect of ablationHepatectomyPatientsCompare treatment effectsClinical treatmentSignificant differencePropensity scoreLogistic regressionPrediction Consistency Regularization for Learning with Noise Labels Based on Contrastive Clustering
Sun X, Zhang S, Ma S. Prediction Consistency Regularization for Learning with Noise Labels Based on Contrastive Clustering. Entropy 2024, 26: 308. PMID: 38667864, PMCID: PMC11049179, DOI: 10.3390/e26040308.Peer-Reviewed Original ResearchConceptsLabel noiseContrastive clusteringConsistency regularizationRegularization termPrediction consistencyClassification accuracyImpact of label noiseEffects of label noiseClassification taskClustering problemComprehensive experimentsNoise labelsLabel informationNeural networkClustering resultsSample recognitionNoise rateMitigate noiseNoiseClassificationModel performanceRegularizationPrototypeAccuracyLabeling
Clinical Trials
Current Trials
Molecular Markers of UV Exposure and Cancer Risk in Skin
HIC ID2000024848RoleSub InvestigatorPrimary Completion Date03/31/2024Recruiting Participants
Academic Achievements & Community Involvement
honor Fellow
International AwardASADetails05/01/2013United Statesactivity Health insurance in mainland China and Taiwan: utilization, impact, and policy interventions
ResearchDetails01/01/2013 - PresentChinaAbstract/SynopsisThe goal of this study is to provide an updated, comprehensive description of health insurance coverage and utilization and their impacts on health and economic outcomes. Special attention has been paid to the less-advantaged groups.
honor Elected Member
International AwardISIDetails06/01/2007United States
News
News
- October 24, 2024
New Analytics Center for Cardiovascular Medicine
- September 19, 2024
YSPH alumna applies biostatistician skills to improve drug outcomes
- June 25, 2024
Terika McCall recognized for inclusive digital health tools
- June 05, 2024
YSPH appoints two new Biostatistics assistant professors
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Biostatistics
PO Box 208034, 60 College Street
New Haven, CT 06520-8034
United States
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60 College Street
Academic Office
Ste 206
New Haven, CT 06510