Wenyi Wang, Ph.D.
Department of Bioinformatics and Computational Biology, Division of Discovery Science
About Dr. Wenyi Wang
Dr. Wenyi Wang is a Professor of Bioinformatics and Computational Biology and Biostatistics at the University of Texas MD Anderson Cancer Center. She received her PhD in Biostatistics from Johns Hopkins University and performed postdoctoral training in statistical genomics at UC Berkeley with Terry Speed and genome technology at Stanford with Ron Davis. Wenyi’s research includes contributions to statistical bioinformatics in cancer, including MuSE for subclonal mutation calling, DeMixT for transcriptomic deconvolution. Recently, she co-led a pan-cancer characterization of genetic intra-tumor heterogeneity in subclonal selection, and led a pan-cancer biomarker identification through integrative deconvolution of transcriptomic/genomic data. Her group is focused on the development of computational methods to study the evolution of cancer cells, and further develop risk prediction models to accelerate the translation of biological findings to clinical practice.
Present Title & Affiliation
Primary Appointment
Professor, Department of Bioinformatics and Computational Biology, Division of Discovery Science, The University of Texas MD Anderson Cancer Center, Houston, TX
Dual/Joint/Adjunct Appointment
Professor, Department of Statistics, Rice University, Houston, TX
Professor, Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX
Professor, Department of Statistics, Texas A&M University, College Station, TX
Professor, Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX
Associate (Tenured) Professor, Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX
Adjust Faculty, Texas A&M University, College Station, TX
Research Interests
The two main research programs in the Wang laboratory are 1) Deconvolution and single-cell modeling for intra- and inter- tumor heterogeneity and 2) Semi-parametric survival modeling for cancer risk prediction.
Education & Training
Degree-Granting Education
| 2007 | Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, US, Biostatistics, Ph.D |
| 2003 | Columbia University College of Physicians and Surgeons, New York City, New York, US, Human Nutrition, MA |
| 2001 | Fudan University, Shanghai, CN, Biology, BS |
Postgraduate Training
| 2023-2025 | FACD Academy, Leadership training, The University of Texas MD Anderson Cancer Center, Houston, Texas |
| 2007-2010 | Postdoctoral Fellow, Statistics, University of California at Berkeley, Berkeley, California |
| 2007-2010 | Postdoctoral Fellow, Genome Technology, Stanford University, Stanford, California |
Experience & Service
Faculty Academic Appointments
Honorary Associate Professor, University College London, London, 2021 - 2024
Visiting Scientist (Sabbatical), The Wellcome Trust Sanger Institute, Hinxton, 2017 - 2017
Associate Professor, Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, 2015 - 2020
Assistant Professor, Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 2010 - 2015
Administrative Appointments/Responsibilities
Program Director, Quantitative Biology Postbaccalaureate Program, The University of Texas MD Anderson Cancer Center, 2025 - Present
Admissions committee member, The University of Texas MD Anderson Cancer Center, Houston, Texas, 2023 - Present
Fellowship Program Director, Department of Statistics, Rice University, Houston, TX, 2023 - 2025
Organizer of the CRC Intergromics working group bi-weekly meeting, The University of Texas MD Anderson Cancer Center, Houston, Texas, 2022 - Present
Associate Editor - Journal of American Statistical Association, American Statistical Association, Virginia, 2022 - Present
Research Enablement, Activities and Conduct (REACT) committee member, The University of Texas MD Anderson Cancer Center, Houston, Texas, 2021 - Present
Admissions Committee Member, Department of Statistics, Rice University, Houston, TX, 2019 - Present
Admissions committee member, Baylor College of Medicine, Houston, TX, 2019 - Present
Admissions Committee member, GSBS, Houston, TX, 2018 - 2020
Academic Standards Committee Member, Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 2016 - 2018
Member of Membership Committee, The University of Texas Graduate School of Biomedical Sciences at Houston, Houston, TX, 2014 - 2016
Quantitative Sciences Program Co-director, The University of Texas Graduate School of Biomedical Sciences at Houston, Houston, TX, 2014 - 2018
Organizer, Internal Seminar Series, Department of Quantitative Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX, 2013 - 2019
Chair, Quality of Life Committee, Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 2010 - 2014
Other Professional Positions
Steering committee member, Outreach officer, American Statistical Association Stats Up Artificial Intelligence Interest Group, 2025 - Present
Consultant, Insilicom, Florida, 2024 - 2025
Affiliated faculty member, MD Anderson Institute of Data Science in Oncology, Houston, TX, 2023 - Present
Promotion referee for Kin Fai Au, Ohio State University Department of Biomedical Informatics, Columbus, OH, 2022 - 2022
Dean search committee member, GSBS, Houston, TX, 2022 - 2022
Kopchick Fellowship Review panalist, GSBS, Houston, TX, 2022
Admissions committee member, Baylor College of Medicine Quantitative and Computational Biology, Houston, TX, 2022 - Present
Faculty member of the TRIUMPH (Translational Research in Multidisciplinary Programs) training program, The University of Texas MD Anderson Cancer Center, Houston, TX, 2021 - Present
Promotion referee for Michael Love, UNC Chapel Hill Department of Biostatistics, Chapel Nill, NC, 2021 - 2021
Promotion referee for Nick Navin, The University of Texas MD Anderson Cancer Center, Houston, TX, 2021
Promotion referee for Roland Schwartz, Max Delbrück Center for Molecular Medicine, Berlin, 2021 - 2021
Collaborator of Colorectal Moonshot Program, The University of Texas MD Anderson Cancer Center, Houston, TX, 2019 - Present
Executive Committee Member, International Chinese Statistical Association, Houston, TX, 2019 - 2020
Program Chair, American Statistical Association Section on Statistical Genetics and Genomics, Alexandria, VA, 2018 - 2018
Educational Program Advisory Committee Member, Eastern North American Region International Biometrics Society, Reston, VA, 2017 - 2018
Faculty member, Baylor College of Medicine Graduate Program in Quantitative Computational Biology, Houston, TX, 2017 - Present
Study section member, National Institute of Health, Bethesda, MD, 2017 - 2021
Study section ad hoc member, National Institute of Health, Bethesda, MD, 2016 - 2017
Interviewer for GSBS program, The University of Texas Graduate School of Biomedical Sciences at Houston, Houston, TX, 2015 - Present
Member, Scientific Program Committee - Subcommittee on Bioinformatics and Computational Biology, American Association of Cancer Research, Philadelphia, PA, 2014 - 2015
Member, Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, TX, 2014 - Present
Member, MD Anderson Center for Genetics and Genomics, Houston, TX, 2014 - 2017
Member, Curriculum Task Force, The University of Texas Graduate School of Biomedical Sciences at Houston, Houston, TX, 2013 - 2014
Reviewer, the George Stancel Fellowship, in computational biology and bioinformatics, The University of Texas Graduate School of Biomedical Sciences at Houston, Houston, TX, 2013
Membership committee member, The International Chinese Statistical Association, Statesboro, GA, 2013 - 2015
Reviewer, graduate program admission, The University of Texas Graduate School of Biomedical Sciences at Houston, Houston, TX, 2012 - 2018
Collaborator, Moon Shot Program in Women's Cancers: Breast and Ovarian, UT MD Anderson Cancer Center, Houston, TX, 2012 - Present
Bioinformatics Faculty Liaison, UT MD Anderson Cancer Center, Houston, TX, 2010 - Present
Consultant, Counsyl, Redwood City, 2008 - 2008
Intramural Institutional Committee Activities
Member, Admissions subcommittee, The University of Texas GSBS, 2025 - Present
Member, Curriculum Committee, The University of Texas GSBS, 2025 - Present
Member, Professionalism Academic Transition Committee, The University of Texas MD Anderson Cancer Center, 2025 - Present
Member, Research Enablement, Activities and Conduct (REACT), The University of Texas MD Anderson Cancer Center, 2021 - Present
Extramural Institutional Committee Activities
Member, Quantitative Science Program Steering Committee, The University of Texas MD Anderson Cancer Center, 2021 - 2023
member, Senate research affairs committee, The University of Texas MD Anderson Cancer Center, 2021 - 2022
Senator, Faculty Senate, The University of Texas MD Anderson Cancer Center, 2021 - 2022
Member, Clinical Research Advisory Committee, The University of Texas MD Anderson Cancer Center, 2015 - 2021
Member, Academic profiles working group, The University of Texas MD Anderson Cancer Center, 2013 - 2013
Member, Faculty search committee (Bioinformatics and Computational Biology), The University of Texas MD Anderson Cancer Center, 2010 - Present
Editorial Activities
Associate Editor, Journal of the American Statistical Association Applications & Case Studies, 2022 - Present
Honors & Awards
| 2025 | Faculty Excellence Award for outstanding contributions in Education and Mentorship Advancement |
| 2025 | Keynote speaker, The 29th Annual International Conference on Research in Computational Molecular Biology Cancer Computational Biology Satellite Meeting, Seoul, South Korea, Apr 24-26, 2025 |
| 2022 | Keynote speaker at RECOMB 2022, The 26th Annual International Conference on Research in Computational Molecular Biology |
| 2021 - 2022 | ASA SSGG Student Paper Award, ASA |
| 2014 | Outstanding service to graduate education, The University of Texas Graduate School of Biomedical Sciences at Houston |
| 2011 | The Stellar Abstract Award, The 5th Annual Program in Quantitative Genomics, Harvard School of Public Health |
| 2008 | Delta Omega Alpha Inducted Member, Johns Hopkins Bloomberg School of Public Health |
| 2008 | Phi Beta Kappa Inducted Member, Johns Hopkins University Chapter of Phi Beta Kappa |
| 2008 | The Jane and Steve Dykacz Award, Johns Hopkins University, Baltimore, MD |
| 2007 | Travel Award, The 11th International Conference on Research in Computational and Molecular Biology |
| 2006 | Travel Award, The International Genetic Epidemiology Society 15th Annual Meeting |
| 2005 | The June B. Culley Award, Johns Hopkins University, Baltimore, MD |
| 1997 - 2001 | People's Scholarship, Fudan University, Shanghai, China |
| 1994 - 2001 | Honor Science Program, Fudan University, Shanghai, China |
Professional Memberships
Selected Presentations & Talks
Regional Presentations
- 2022. Cancer Risk Prediction Modeling and Deciphering Cancer Evolution and Ecology. Conference. The UTGSBS Quantitative Sciences Program, US.
- 2022. Tumor Cell Total mRNA Expression Shapes the Molecular and Clinical Phenotype of Cancer. Conference. Enjoy Science Webinar Series UT MD Anderson Cancer Center, US.
- 2016. Gene expression deconvolution in heterogeneous tumor samples (TCGA) using DeMix-Bayes. Conference. TCGA PanCanAtlas Tumor Heterogeneity and Evolution AWG, US.
- 2016. Gene expression deconvolution in heterogeneous tumor samples (TCGA) using DeMix-Bayes. Conference. MDACC, US.
- 2016. Gene expression deconvolution in heterogeneous tumor samples (TCGA) using DeMix-Bayes. Conference. TCGA PanCanAtlas Immune Response AWG, US.
- 2016. Cancer-specific Characterization in Families with Li-Fraumeni Syndrome. Conference. MDACC, US.
- 2016. Gene expression deconvolution in heterogeneous tumor samples using DeMix-Bayes. Conference. MDACC, US.
- 2016. Modeling multiple primary cancers over time using a novel familywise likelihood under the non-homogeneous Poisson process. Conference. MDACC, US.
- 2015. Assessing Risk in Families with Cancer. Conference. STEAM organization, US.
- 2015. Cell type-specific Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration using Expression Data. Conference. MDACC, US.
- 2011. Statistical methods for DNA resequencing analysis in disease gene studies. Conference. 2011 Conference of Texas Statisticians. College Station, TX, US.
National Presentations
- 2025. Deciphering tumor heterogeneity for benefits from immunotherapy in cancer. Invited. Department of Statistics, US.
- 2025. Deciphering tumor heterogeneity for benefits from immunotherapy in cancer. Invited. Department of Biomedical Informatics, US.
- 2025. A Guide to Transcriptomic Deconvolution in Cancer. Invited. CGSI, US.
- 2025. Deciphering tumor heterogeneity for benefits from immunotherapy in cancer. Invited. Department of Genetics, US.
- 2025. Deciphering tumor heterogeneity for benefits from immunotherapy in cancer. Invited. Enjoy Science Webinar series. Houston, Texas, US.
- 2024. Cancer risk modeling for deleterious mutations in TP53 using a multi-center consortium. Invited. LFS Association Symposium. Philladelphia, US.
- 2024. Deciphering cancer cell evolution and ecology through mathematical deconvolution. Invited. Physics, biology and systems biology seminar series. New York City, US.
- 2024. A Roadmap to Transcriptomic Deconvolution in Cancer. Invited. Department of Statistics. Lexington, US.
- 2024. A Guide to Perform Transcriptomic Deconvolution in Cancer. Invited. Computational Genetics Summer Institute. Los Angeles, US.
- 2024. When Evolution Meets Tumor Immunogenicity. Invited. Coffey Holden Prostate Cancer Academy. Los Angeles, US.
- 2024. Probability in Bioinformatics and Genetics. Panelist. Statistical Curriculum Retreat. Ann Arbor, MI, US.
- 2024. Cancer risk modeling for deleterious mutations in TP53 using a multi-center consortium. Invited. SSACB. Bethesda, MD, US.
- 2023. Statistical methods development for cancer risk prediction. Invited. Computational Genetics Summer Institute. Los Angeles, US.
- 2023. Characterizing tumor microenvironment and clonal expansion at single cell resolution. Conference. ENAR. Nashville, TN, US.
- 2023. Benchmarking-related model development for deconvoluting cancer genomes and heterogeneous tissue transcriptomes. Conference. National Cancer Institute Spring School on Algorithms for Cancer Biology. Bethesda, MD, US.
- 2022. Computational deconvolution of cancer genomes and transcriptomes. Conference. Computational Genomics Summer Institute at UCLA. Los Angeles, CA, US.
- 2022. Deciphering cancer cell evolution and ecology. Conference. RECOMB Keynote Speaker. San Diego, CA, US.
- 2021. Tumor cell total mRNA expression shapes the molecular and clinical phenotype of cancer. Conference. Endocrinology Society, US.
- 2021. Tumor cell total mRNA expression shapes the molecular and clinical phenotype of cancer. Conference. ENAR, US.
- 2020. Tumor cell total mRNA expression shapes the molecular and clinical phenotype of cancer. Conference. ICSA 2020 Applied Statistics Symposium (virtual). Houston, TX, US.
- 2020. Tumor cell total mRNA expression shapes the molecular and clinical phenotype of cancer. Conference. MD Anderson Cancer Center Symposium on Cancer Research: Leading Edge in Cancer Research (virtual). Houston, TX, US.
- 2018. Deconvolution of multi-omics Data from Heterogeneous Tumor Samples. Invited. TAMU Bioinformatics & Cancer Symposium. College Station, TX, US.
- 2018. A study on de novo mutations in TP53 using families with Li-Fraumeni syndrome. Invited. Systems Genetics Cancer Workshop. Portland, OR, US.
- 2018. Robust subclonal architecture reconstruction from 2,700 cancer genomes. Invited. ENAR. Atlanta, GA, US.
- 2015. Gene expression deconvolution in heterogeneous tumor samples using DeMix-Bayes. Invited. NCI DCCPS New Grantee Workshop. Bethesda, MD, US.
- 2015. Bayesian variable selection for binary outcomes in high dimensional settings. Conference. Joint Statistical Meeting. Seattle, WA, US.
- 2015. Statistical methods for analysis of genomic data from heterogeneous cancer samples. Invited. BioC 2015. Seattle, WA, US.
- 2014. DeMix-Bayes: A Bayesian model for the deconvolution of mixed cancer transcriptomes in microarray and RNA sequencing data. Invited. NCI TCGA network meeting. Bethesda, MD, US.
- 2014. MuSE: somatic evolution estimation for mutation calling in sequencing data of matched tumor-normal samples. Invited. The Joint Statistical Meetings. Boston, MA, US.
- 2014. Discussant for Keynote Speaker: Two Aspects in Tumor Heterogeneity: Subclonal Mutations and Stromal Expression. Invited. The Southern Regional Council on Statistics Summer Research Conference. Galveston, TX, US.
- 2014. Gene expression deconvolution in heterogeneous tumor samples. Invited. 7th Annual Bayesian Biostatistics and Bioinformatics Conference. Houston, TX, US.
- 2013. Personalized risk assessment for families with Li-Fraumeni Syndromes. Invited. NCI. Boston, MA, US.
- 2013. Gene expression deconvolution in heterogeneous tumor samples. Conference. ASHG. Boston, MA, US.
- 2013. Gene expression deconvolution in heterogeneous tumor samples. Conference. The Cancer Genome Atlas Semi-Annual Steering Committee Meeting. Bethesda, MD, US.
- 2013. DeMix: Deconvolution for Mixed Cancer Transcriptomes Using Raw Measured Data. Invited. ENAR. Orlando, FL, US.
- 2012. Determining probability of rare variants: implications for designs of family-based sequencing studies. Invited. The Joint Statistical Meetings. San Diego, CA, US.
- 2012. Determining Probability of Rare Variants: Design Implications for Family-based Sequencing Studies. Invited. Southern Regional Council on Statistics. Jekyll Island, FL, US.
- 2011. Validating Risk Prediction Models Using Family Registries. Invited. Fourth Annual Bayesian Biostatistics Conference. Houston, TX, US.
- 2010. Validating Risk Prediction Models using Markov chain Monte Carlo (poster). Conference. American Society of Human Genetics. Washington, DC, US.
- 2010. Identification of Rare DNA Variants in Mitochondrial Disorders with Improved Array-based Resequencing. Conference. Cold Spring Harbor Laboratory. Cold Spring Harbor, NY, US.
International Presentations
- 2025. Subclonal mutation load predicts survival and response to immunotherapy in cancers with low to moderate tumor mutation burden. Invited. EMBL Cancer Genomics. Heidelberg, DE.
- 2025. Deciphering tumor heterogeneity for benefits from immunotherapy in cancer. Invited. RECOMB Computational Cancer Biology. Seoul, KR.
- 2024. Deciphering cancer cell evolution and ecology and their associations with cancer prognosis. Invited. Shanghai, CN.
- 2024. Statistics and AI. Invited. Statistics and AI - A Fireside Conversation, US.
- 2024. Computational Biology in Cancer. Invited. 1st MBZUAI Workshop on Statistics for the Future of AI. Abu Dabi, AE.
- 2023. Estimating tumor-cell total mRNA expression through deconvolution. Conference. BIRS workshop. Banff, CA.
- 2023. Deciphering cancer cell evolution and ecology and their association with cancer prognosis. Conference. AACR. Orlando, US.
- 2022. Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression. Conference. The 4th Annual Meeting of China Chapter of the International MAQC Society, CN.
- 2022. Cancer risk modeling for deleterious mutations in TP53 using a multi-center consortium. Conference. 6th International LFS Association Symposium. Bethesda, US.
- 2020. Tumor cell total mRNA expression shapes the molecular and clinical phenotype of cancer. Conference. Victorian Cancer Bioinformatics Symposium (virtual). Melbourne, AU.
- 2020. Clinical Impact of de novo mutations in TP53 as illustrated in families with Li-Fraumeni syndrome. Invited. 5th international LFS Association Symposium (virtual). Boston, US.
- 2019. Transcriptomic deconvolution for tumor-stroma-immune interactions across cancer (sub)types. Invited. Cancer System Genetics Workshop. Berlin, DE.
- 2019. Mutation-based expression deconvolution identifies differential transcriptional activity across cancer (sub)types. Invited. Towards In silico-Guided Clinical Trial in Cancer Workshop. Oslo, NO.
- 2019. Ares: allele read specific expression estimation for somatic mutations in a pancancer study. Invited. Single Cell and Massively Parallel Approaches Workshop. Bridgetown, BB.
- 2018. Cancer risk assessment and penetrance estimation for deleterious mutations in TP53 using a multi- centre consortium. Invited. The 4th International Li-Fraumeni Syndrome Conference. Toronto, CA.
- 2017. Robust subclonal architecture reconstruction from 2,700 cancer genomes. Invited. CMO-BIRS: Chal- lenges and Synergies in the Analysis of Large-Scale Population-Based Biomedical Data. Oaxaca, MX.
- 2017. Statistical inference problems for the gene expression deconvolution of heterogeneous tumor sam- ples. Invited. Systems Genetics of Cancer. London, GB.
- 2017. Statistical methods for the gene expression deconvolution of heterogeneous tumor sample. Invited. BIRS workshop: Statistical and Computational Challenges in Large Scale Molecular Biology. Banff, CA.
- 2016. Cancer-specific characterization of Li-Fraumeni Syndrome. Invited. The 2016 International LFS conference & the 3rd annual LiFE consortium and LFS association conference. Columbus, US.
- 2015. Bayesian variable selection for binary outcomes in high dimensional settings. Invited. 8th International Conference of the ERCIM WG on Computational and Methodological Statistics. London, GB.
- 2015. Statistical methods for analysis of genomic data from heterogeneous cancer samples. Invited. International Bioinformatics Workshop. Harbin, CN.
- 2014. Bayesian variable selection for binary outcomes in high dimensional settings. Invited. The International Society of Bayesian Analysis. Cancun, MX.
- 2013. Gene expression deconvolution in heterogeneous tumor samples. Invited. The International Chinese Statistical Association. HongKong, CN.
- 2013. Gene expression deconvolution in heterogeneous tumor samples. Invited. The Joint Statistical Meeting. Montreal, CA.
- 2012. Determining probability of rare variants in sequencing studies for familial cancer syndromes. Invited. Jilin University. Changchun, CN.
- 2011. Determining probability of rare variants: implications for designes of family-based sequencing studies. Invited. University of Maryland. Silver Spring, US.
- 2011. Determining probability of germline mutations in family-based sequencing studies. Invited. The First Wuxi International Statistics Forum. Wuxi, CN.
- 2011. Statistical Methods for DNA Resequencing. Conference. Meeting of the International Biometric Society. Miami, US.
Formal Peers
- 2023. An integrated genomic definition and therapeutic strategy for heterogeneous tumors. Invited. Memphis, TN, US.
- 2023. Deciphering cancer cell evolution and ecology. Invited. Bethesda, MD, US.
- 2022. Risk Prediction Models of Li-Fraumeni Syndrome for Genetic Counseling. Invited, US.
- 2022. Cancer risk modeling for deleterious mutations in TP53 using a multi-center consortium. Invited. Houston, TX, US.
- 2021. Tumor Cell Total mRNA Expression Shapes the Molecular and Clinical Phenotype of Cancer. Invited. Bethesda, MD, US.
- 2021. Statistical methods for genomic analysis of heterogeneous tumor samples. Invited, US.
- 2021. Tumor cell total mRNA expression shapes the molecular and clinical phenotype of cancer. Invited. San Diego, CA, US.
- 2021. Tumor cell total mRNA expression shapes the molecular and clinical phenotype of cancer. Invited. Dallas, TX, US.
- 2020. Understanding Tumor Transcriptional Activity, Heterogeneity and Evolution using Deconvolution Models. Invited. New York, NY, US.
- 2019. Global tumor transcriptional activity reveals aggressiveness across multiple cancers. Invited. Seattle, WA, US.
- 2019. Global tumor transcriptional activity reveals aggressiveness across multiple cancers. Invited. Los Angeles, CA, US.
- 2019. Global tumor transcriptional activity reveals aggressiveness across multiple cancers. Invited. Oxford, GB.
- 2019. Robust subclonal architecture reconstruction from ∼ 2,700 cancer genomes. Invited. Nashville, TN, US.
- 2019. Robust subclonal architecture reconstruction from ~2,700 cancer genomes. Invited. Los Angeles, CA, US.
- 2019. Statistical methods for the deconvolution of transcriptomes and genomes from heterogeneous tumor samples. Invited. Houston, TX, US.
- 2018. Robust subclonal architecture reconstruction from ∼ 2,700 cancer genomes. Invited. Houston, TX, US.
- 2018. Statistical methods for the deconvolution of high-throughput sequencing data from heterogeneous tumor samples. Invited. Bethesda, MD, US.
- 2018. Statistical methods for the deconvolution of high-throughput sequencing data from heterogeneous tumor samples. Invited. Warwick, GB.
- 2018. Statistical methods for the deconvolution of high-throughput sequencing data from heterogeneous tumor samples. Invited. London, GB.
- 2018. Statistical methods for the deconvolution of mixed cancer transcriptomes. Invited. Houston, TX, US.
- 2017. Robust subclonal architecture reconstruction from ∼2,700 cancer genomes. Invited. Shanghai, CN.
- 2017. Statistical methods for the deconvolution of mixed cancer transcriptomes. Invited. Boston, MA, US.
- 2017. Robust subclonal architecture reconstruction from ∼2,700 cancer genomes. Invited. Los Angelos, CA, US.
- 2017. Statistical methods for the gene expression deconvolution of heterogeneous tumor sample. Invited. Heidelberg, DE.
- 2017. Statistical methods for the gene expression deconvolution of heterogeneous tumor sample. Invited. Cambridge, GB.
- 2017. Statistical methods for the gene expression deconvolution of heterogeneous tumor sample. Invited. Oxford, GB.
- 2017. Statistical methods for the gene expression deconvolution of heterogeneous tumor sample. Invited. Houstno, TX, US.
- 2017. Accounting for Tumor Heterogeneity Using a Sample-Specific Error Model Improves Sensitivity and Specificity in Mutation Calling for Sequencing Data. Invited. Cedar Rapids, IA, US.
- 2017. Statistical methods for the gene expression deconvolution of heterogeneous tumor sample. Invited. Houston, TX, US.
- 2016. Accounting for Tumor Heterogeneity Using a Sample-Specific Error Model Improves Sensitivity and Specificity in Mutation Calling for Sequencing Data. Invited. Evry, FR.
- 2016. Gene expression deconvolution in heterogeneous tumor samples (TCGA) using DeMix-Bayes. Invited, US.
- 2016. Accounting for Tumor Heterogeneity Using a Sample-Specific Error Model Improves Sensitivity and Specificity in Mutation Calling for Sequencing Data. Invited, US.
- 2016. Accounting for Tumor Heterogeneity Using a Sample-Specific Error Model Improves Sensitivity and Specificity in Mutation Calling for Sequencing Data. Invited, US.
- 2016. Accounting for Tumor Heterogeneity Using a Sample-Specific Error Model Improves Sensitivity and Specificity in Mutation Calling for Sequencing Data. Invited, US.
- 2016. Accounting for Tumor Heterogeneity Using a Sample-Specific Error Model Improves Sensitivity and Specificity in Mutation Calling for Sequencing Data. Invited, US.
- 2016. Accounting for tumor heterogeneity using a sample-specific error model improves sensitivity and specificity in mutation calling for sequencing data. Invited. Leuven, BE.
- 2015. Gene expression deconvolution of heterogeneous tumor samples: DeMix-Bayes. Invited. London, GB.
- 2015. Cancer-specific characterization of the Li-Fraumeni syndrome. Invited. Sau Paulo, BR.
- 2015. Statistical methods for analysis of genomic data from heterogeneous cancer samples. Invited. Essonne, FR.
- 2015. Statistical methods for analysis of genomic data from heterogeneous cancer samples. Invited. Baltimore, MD, US.
- 2015. Somatic Mutation Calling and Gene Expression Deconvolution in Heterogeneous Tumor Samples. Invited. Oxford, GB.
- 2015. LFSpro: a risk assessment tool to estimate TP53 mutation status in families with Li-Fraumeni Syndrome. Invited. Houston, TX, US.
- 2015. DeMix-Bayes: A Bayesian model for the deconvolution of mixed cancer transcriptomes in microarray and RNA sequencing data. Invited. Houston, TX, US.
- 2014. Fudan University Key Laboratory Senior Visiting Scholarship. Visiting. Shanghai, CN.
- 2014. Cancer-specific characterization of the Li-Fraumeni Syndrome. Invited. South Hadley, MA, US.
- 2014. Cancer-specific characterization of the Li-Fraumeni Syndrome. Invited. Worcester, MA, US.
- 2014. Somatic Mutation Calling and Gene Expression Deconvolution in Heterogeneous Tumor Samples. Invited. Oxford, GB.
- 2014. Gene expression deconvolution in heterogeneous tumor samples. Invited. College Station, TX, US.
- 2013. Fudan University Key Laboratory Senior Visiting Scholarship. Visiting. Shanghai, CN.
- 2012. Rare variant detection using family-based sequencing analysis. Invited. St. Louis, MO, US.
- 2011. Determining Probability of Rare Variants: Implications for Designs of Family-based Sequencing Studies. Invited. Houston, TX, US.
- 2011. Statistical Methods for DNA Resequencing Analysis in Disease-Gene Studies. Invited. Houston, TX, US.
- 2011. Statistical Methods for DNA Resequencing Analysis in Disease-Gene Studies. Invited. Shanghai, CN.
- 2011. Statistical Methods for DNA Resequencing Analysis in Disease-Gene Studies. Invited. Houston, TX, US.
- 2011. Statistical Methods for DNA Resequencing Analysis in Disease-Gene Studies. Invited. Shanghai, CN.
- 2011. Statistical Methods for DNA Resequencing Analysis in Disease-Gene Studies. Invited. Shanghai, CN.
- 2010. Statistical Methods for DNA Resequencing Analysis in Disease-Gene Studies. Invited. Shanghai, CN.
- 2010. Statistical Methods for DNA Resequencing Analysis in Disease-Gene Studies. Invited. Shanghai, CN.
- 2010. Statistical Methods for DNA Resequencing Analysis in Disease-Gene Studies. Invited. Boston, MA, US.
- 2010. Statistical Methods for DNA Resequencing Analysis in Disease-Gene Studies. Invited. New York, NY, US.
- 2010. Statistical Methods for DNA Resequencing Analysis in Disease-Gene Studies. Invited. Houston, TX, US.
- 2010. Statistical Methods for DNA Resequencing Analysis in Disease-Gene Studies. Invited. Boston, MA, US.
- 2010. Statistical Methods for DNA Resequencing Analysis in Disease-Gene Studies. Invited. Providence, RI, US.
- 2010. Statistical Methods for DNA Resequencing Analysis in Disease-Gene Studies. Invited. Nashville, TN, US.
- 2009. Detecting Rare Variants in Candidate Genes for Mitochondrial Diseases using Resequencing Arrays. Invited. Berkeley, CA, US.
- 2009. Detecting Rare Variants in Candidate Genes for Mitochondrial Diseases using Resequencing Arrays. Invited. Palo Alto, CA, US.
Grant & Contract Support
| Date: | 2025 - 2026 |
| Title: | Conformal inference of risk prediction in benefit from immunotherapy |
| Funding Source: | Joint Rice University-MD Anderson Cancer Center Cancer Bioengineering Collaborative Seed Grant Program |
| Role: | PI |
| Date: | 2022 - 2026 |
| Title: | An integrated genomic definition and therapeutic strategy for androgen indifferent prostate cancers |
| Funding Source: | Department of Defense (DOD) |
| Role: | PI |
| ID: | W81XWH2210258 |
| Date: | 2022 - 2027 |
| Title: | Statistical methods for genomic analysis of heterogeneous tumors |
| Funding Source: | NIH/NCI |
| Role: | PI |
| ID: | 5 R01 CA268380-02 |
Selected Publications
Book Chapters
- Ji S, Montierth MD, Wang W. MuSE: A Novel Approach to Mutation Calling with Sample-Specific Error Modeling. In: Methods Mol Biol, 21-27, 2022.
- Wang W, Fan Y, Speed T. DNA Variant Calling in Targeted Sequencing Data. In: Advances in Statistical Bioinformatics: Models and Integrative Inference for High-Throughput. Cambridge University Press, 2013.
Patient Reviews
CV information above last modified April 01, 2026