Shuangge Steven Ma, PhD
Department Chair and Professor of BiostatisticsDownloadHi-Res Photo
Cards
Education
PhD
University of Wisconsin (2004)
University of Wisconsin (2004)
MS
University of California at Los Angeles (2000)
University of California at Los Angeles (2000)
Contact Info
Training
Postdoctoral Associate
University of Washington (2006)
University of Washington (2006)
Education
PhD
University of Wisconsin (2004)
University of Wisconsin (2004)
MS
University of California at Los Angeles (2000)
University of California at Los Angeles (2000)
Contact Info
Training
Postdoctoral Associate
University of Washington (2006)
University of Washington (2006)
Education
PhD
University of Wisconsin (2004)
University of Wisconsin (2004)
MS
University of California at Los Angeles (2000)
University of California at Los Angeles (2000)
Contact Info
Training
Postdoctoral Associate
University of Washington (2006)
University of Washington (2006)
About
Copy Link
Titles
Department Chair and Professor of Biostatistics
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.
Last Updated on April 07, 2025.
Appointments
Biostatistics
ChairDualBiostatistics
ProfessorPrimary
Other Departments & Organizations
- All Institutions
- Biostatistics
- Cancer Prevention and Control
- 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 Ventures
- Yale-BI Biomedical Data Science Fellowship
- 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
Copy Link
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, investigating health insurance utilization and impact;
Provide statistical support to multiple biomedical studies.
Medical Research Interests
Biostatistics; Economics; Neoplasms
Public Health Interests
Mental Health; Bioinformatics; Biomarkers; Cancer; Cardiovascular Diseases; Genetics, Genomics, Epigenetics; Health Policy
ORCID
0000-0001-9001-4999
Research at a Glance
Research Interests
Research topics Shuangge Steven Ma is interested in exploring.
Publications
2026
Heterogeneous Gene Network Estimation for Single-Cell Transcriptomic Data via a Joint Regularized Deep Neural Network
Yang J, Li T, Wang T, Ma S, Wu M. Heterogeneous Gene Network Estimation for Single-Cell Transcriptomic Data via a Joint Regularized Deep Neural Network. Journal Of The American Statistical Association 2026, ahead-of-print: 1-12. DOI: 10.1080/01621459.2026.2615185.Peer-Reviewed Original ResearchCitationsConceptsSingle-cell transcriptomic dataStatistical enrichmentTranscriptome dataEnrichment of biological processesCellular heterogeneityGene network estimationState-of-the-art methodsRegularized deep neural networkDeep neural network methodState-of-the-artDeep neural networksSingle-cell resolutionNetwork estimationGene networksNeural network methodHub genesK-means clusteringNetwork constructionBiological interpretationBiological processesMultiple tissuesHidden layerNeural networkGenesMultiple networksLatent space modeling for human disease network with temporal variations: Analysis of medicare data
Zhu G, Wang R, Li R, Zhang S, Ma S, Qiao G, Mei H. Latent space modeling for human disease network with temporal variations: Analysis of medicare data. The Annals Of Applied Statistics 2026, 20: 364-384. DOI: 10.1214/25-aoas2121.Peer-Reviewed Original ResearchA Taxonomy for Assessing Real-World Targeted Cancer Therapy Options in the Context of Broad Genomic Profiling.
Wang X, Long J, Rothen J, Huang S, Soulos P, Goldberg S, Robinson T, Ma S, Mamtani R, Presley C, Wang S, Kunst N, Gross C, Dinan M. A Taxonomy for Assessing Real-World Targeted Cancer Therapy Options in the Context of Broad Genomic Profiling. Journal Of The National Comprehensive Cancer Network 2026, 24 PMID: 41698347, DOI: 10.6004/jnccn.2025.7131.Peer-Reviewed Original ResearchThis study introduces Y-MATRIX, a taxonomy classifying genomic profiling results by clinical actionability, showing increased actionable findings in advanced lung cancer from 2017 to 2023.Integrating Omics and Pathological Imaging Data for Cancer Prognosis via a Deep Neural Network‐Based Cox Model
Li J, Ma S. Integrating Omics and Pathological Imaging Data for Cancer Prognosis via a Deep Neural Network‐Based Cox Model. Statistics In Medicine 2026, 45: e70435. PMID: 41641685, DOI: 10.1002/sim.70435.Peer-Reviewed Original ResearchDNN-based semiparametric AFT model for integrating genomic and pathological imaging data in cancer prognosis
Li J, Zhang Q, Ma S. DNN-based semiparametric AFT model for integrating genomic and pathological imaging data in cancer prognosis. Biometrics 2026, 82: ujag045. PMID: 41837305, PMCID: PMC13016944, DOI: 10.1093/biomtc/ujag045.Peer-Reviewed Original ResearchMeSH Keywords and Concepts
2025
Network-based hierarchical heterogeneity analysis and applications to cancer omics data
Wang R, Zhang S, Ma S. Network-based hierarchical heterogeneity analysis and applications to cancer omics data. Science China Mathematics 2025, 1-14. DOI: 10.1007/s11425-024-2435-2.Peer-Reviewed Original ResearchJoint identification of spatially variable genes via a network-assisted Bayesian regularization approach
Wu M, Li Y, Ma S, Wu M. Joint identification of spatially variable genes via a network-assisted Bayesian regularization approach. The Annals Of Applied Statistics 2025, 19: 2705-2723. DOI: 10.1214/25-aoas2097.Peer-Reviewed Original ResearchCitationsConceptsSpatially variable genesVariable genesSpatial transcriptomics dataTranscriptome dataConfounding variationMultiple genesMechanistic functionBiological processesGenesReal data analysesCellular distributionZero-inflated negative binomial distributionRegularization approachBiological understandingJoint identificationCount natureResults of simulation studiesNegative binomial distributionZero inflationMCMC algorithmBayesian regularization approachCellular compositionSpatial patternsBinomial distributionSimulation studyConditional Graphical Models With A Hierarchical Sparse Estimation
Li R, Zhang Q, Ma S. Conditional Graphical Models With A Hierarchical Sparse Estimation. Statistics And Computing 2025, 36: 30. DOI: 10.1007/s11222-025-10783-8.Peer-Reviewed Original ResearchOrdinal Sparse Neural Networks for Modeling Gene‐ and Imaging‐Environment Interactions
Xue J, Xu Y, Li J, Ma S, Fang K. Ordinal Sparse Neural Networks for Modeling Gene‐ and Imaging‐Environment Interactions. Statistics In Medicine 2025, 44: e70302. PMID: 41105049, DOI: 10.1002/sim.70302.Peer-Reviewed Original ResearchMeSH Keywords and ConceptsAnalysis of cross-platform health communication with a network approach
Fan X, Liu M, Ma S. Analysis of cross-platform health communication with a network approach. Biometrics 2025, 81: ujaf154. PMID: 41273214, DOI: 10.1093/biomtc/ujaf154.Peer-Reviewed Original ResearchMeSH Keywords and ConceptsConceptsOnline health communitiesWord frequency vectorsHealth communicationOnline platformsCommunication modelTwitter topicsTwitterCo-occurrenceNetwork approachFrequency vectorInformation sourcesMedical informationStructure contentCommunicationEarly analysisWord frequencyNetworkCo-occurrence networkCommunication analysisComplex medical informationNumerical propertiesPlatform
Clinical Trials
Current Trials
Molecular Markers of UV Exposure and Cancer Risk in Skin
IRB ID2000024848RoleSub InvestigatorPrimary Completion Date03/31/2024Recruiting Participants
Academic Achievements & Community Involvement
Copy Link
News
Copy Link
News
- July 23, 2025
Digging into data science at the 38th Annual New England Statistical Symposium
- May 06, 2025
Genetic Test Underused in Cancer Care
- January 07, 2025
Leadership Appointments Underscore Yale Biostatistics’ Global Strength in Research and Innovation
- October 24, 2024
New Analytics Center for Cardiovascular Medicine
Get In Touch
Copy Link
Contacts
Locations
300 George Street
Academic Office
Ste 501
New Haven, CT 06511