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Dr. Hongyu Zhao is the Ira V. Hiscock Professor of Biostatistics and Professor of Statistics and Data Science and Genetics, Chair of the Biostatistics Department and the Co-Director of Graduate Studies of the Inter-Departmental Program in Computational Biology and Bioinformatics at Yale University. He received his B.S. in Probability and Statistics from Peking University in 1990 and Ph.D. in Statistics from the University of California at Berkeley in 1995. His research interests are the developments and applications of novel statistical methods to address scientific questions in genetics, molecular biology, drug developments, and precision medicine.

Some of his recent projects include large scale genome wide studies to identify genetic variants underlying complex diseases, genetic risk prediction, single cell analysis, biological network modeling and analysis, disease biomarker identification, genome annotation, cancer genomics, microbiome analysis, and image analysis. He has published over 560 articles in statistics, human genetics, bioinformatics, and proteomics, and edited two books on human genetics analysis and statistical genomics. He has trained over 80 doctoral and post-doctoral students, many of whom are holding tenured or tenure-track positions at major universities in the states and overseas.

Dr. Zhao is a Co-Editor of the Journal of the American Statistical Association Theory and Methods, and serves on the editorial boards of several leading statistical and genetics journals. He was the recipient of the Mortimer Spiegelman Award for a top statistician in health statistics under the age of 40 awarded by the American Public Health Association and the Pao-Lu Hsu Award from the International Chinese Statistical Association. His research has also been recognized by the Evelyn Fix Memorial Medal and Citation by UC Berkeley, a Basil O'Connor Starter Scholar Award by the March of Dimes Foundation, election to the fellowship of the American Association for the Advancement of Science, the American Statistical Association, and the Institute of Mathematical Statistics.

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Genome Wide Association Studies: We are developing statistical methods to integrate diverse data types and prior biological knowledge to identify genes and variants for common diseases and risk prediction models. We also develop methods to infer the genetic architecture of complex diseases and for risk predictions.
Single Cell Analysis: We are developing statistically robust and computationally efficient methods for single cell data with the objectives of inferring genetic regulation and signaling at the single cell level, and the identifications of cellular changes across different conditions.
Network Modeling: We are developing statistical methods to model biological networks under the general framework of Gaussian and other graphical models. Specific networks we are working on include gene expression regulatory networks, signaling networks, and eQTL networks.
Imaging Genetics: We focus on the analysis of data from several consortia to infer the impacts of genetics on imaging traits.

Wearable Device: We are developing methods to extract signals from wearable devices and then combine them with genetics data to infer the genetic basis of activity and sleeping traits.\\n
Cancer Genomics: We are developing statistical and computational methods to analyze cancer genomics data, e.g. microarrays and next generation sequencing, to identify cancer subtypes, driver mutations, and appropriate treatments for cancer patients.
Microbiome Analysis: We are developing modeling and analysis approaches for microbiome data generated from next generation sequencing data.
Proteomics: Our current focus is on targeted proteomics, such as Multiple Reaction Monitoring.

\",\"summary\":\"

Our research is driven by the need to analyze and interpret large and complex data sets in biomedical research. For example, in genome wide association studies involving thousands to hundreds of thousands of individuals, millions of DNA variants are analyzed for each person. Such data offer researchers the opportunity to identify genes and variants affecting disease susceptibility and develop risk prediction models to facilitate disease prevention, monitoring, and treatment. There are many statistical challenges arising from the analysis of such data, including the very high dimensionality of the markers, the relatively weak signals, and the need to incorporate prior knowledge and other data sets in analysis. Other examples include the analyses of next generation sequence data, single cell data, image data, microbiome data, wearable device data, and electronic medical records, which present even greater statistical and computational challenges. Our group has been developing statistical methods to address these challenges, such as empirical Bayes methods to borrow information across different data sets, different generalizations of Gaussian graphical models for network inference, Markov random field models for spatial and temporal modeling, and general machine learning methods for high dimensional data.\\n

Specialized Terms: Statistical genomics and proteomics; Bioinformatics; Data integration; High dimensional data; Network and graphical models; Disease risk prediction; Microbiome; Cancer genomics; Single cell analysis; Imaging genetics; Wearable device; Electronic medical records\\n

\",\"researchInterests\":[\"Genetics\",\"Public Health\",\"Computational Biology\",\"Statistics\",\"Genomics\",\"Proteomics\",\"Biostatistics\",\"Single-Cell Analysis\",\"Microbiota\",\"Wearable Electronic Devices\"],\"healthInterests\":[\"Genetics, Genomics, Epigenetics\"],\"researchImagesGallery\":null},\"publications\":[{\"title\":\"NITUMID: Nonnegative matrix factorization-based Immune-TUmor MIcroenvironment Deconvolution.\",\"citation\":\"Tang D, Park S, Zhao H. NITUMID: Nonnegative matrix factorization-based Immune-TUmor MIcroenvironment Deconvolution. Bioinformatics (Oxford, England) 2020, 36:1344-1350.\",\"year\":2020,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/31593244\",\"text\":\"NITUMID: Nonnegative matrix factorization-based Immune-TUmor MIcroenvironment Deconvolution.\"}},{\"title\":\"A statistical framework for cross-tissue transcriptome-wide association analysis.\",\"citation\":\"Hu Y, Li M, Lu Q, Weng H, Wang J, Zekavat SM, Yu Z, Li B, Gu J, Muchnik S, Shi Y, Kunkle BW, Mukherjee S, Natarajan P, Naj A, Kuzma A, Zhao Y, Crane PK, Lu H, Zhao H. A statistical framework for cross-tissue transcriptome-wide association analysis. Nature Genetics 2019, 51:568-576.\",\"year\":2019,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/30804563\",\"text\":\"A statistical framework for cross-tissue transcriptome-wide association analysis.\"}},{\"title\":\"Prediction analysis for microbiome sequencing data.\",\"citation\":\"Wang T, Yang C, Zhao H. Prediction analysis for microbiome sequencing data. Biometrics 2019, 75:875-884.\",\"year\":2019,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/30994187\",\"text\":\"Prediction analysis for microbiome sequencing data.\"}},{\"title\":\"Spectral clustering based on learning similarity matrix.\",\"citation\":\"Park S, Zhao H, Birol I. Spectral clustering based on learning similarity matrix. Bioinformatics (Oxford, England) 2018, 34:2069-2076.\",\"year\":2018,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/29432517\",\"text\":\"Spectral clustering based on learning similarity matrix.\"}},{\"title\":\"Spatiotemporal transcriptomic divergence across human and macaque brain development.\",\"citation\":\"Zhu Y, Sousa AMM, Gao T, Skarica M, Li M, Santpere G, Esteller-Cucala P, Juan D, Ferrández-Peral L, Gulden FO, Yang M, Miller DJ, Marques-Bonet T, Imamura Kawasawa Y, Zhao H, Sestan N. Spatiotemporal transcriptomic divergence across human and macaque brain development. Science (New York, N.Y.) 2018, 362.\",\"year\":2018,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/30545855\",\"text\":\"Spatiotemporal transcriptomic divergence across human and macaque brain development.\"}},{\"title\":\"Systematic tissue-specific functional annotation of the human genome highlights immune-related DNA elements for late-onset Alzheimer's disease.\",\"citation\":\"Lu Q, Powles RL, Abdallah S, Ou D, Wang Q, Hu Y, Lu Y, Liu W, Li B, Mukherjee S, Crane PK, Zhao H. Systematic tissue-specific functional annotation of the human genome highlights immune-related DNA elements for late-onset Alzheimer's disease. PLoS Genetics 2017, 13:e1006933.\",\"year\":2017,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/28742084\",\"text\":\"Systematic tissue-specific functional annotation of the human genome highlights immune-related DNA elements for late-onset Alzheimer's disease.\"}},{\"title\":\"Leveraging functional annotations in genetic risk prediction for human complex diseases.\",\"citation\":\"Hu Y, Lu Q, Powles R, Yao X, Yang C, Fang F, Xu X, Zhao H. Leveraging functional annotations in genetic risk prediction for human complex diseases. PLoS Computational Biology 2017, 13:e1005589.\",\"year\":2017,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/28594818\",\"text\":\"Leveraging functional annotations in genetic risk prediction for human complex diseases.\"}},{\"title\":\"A Dirichlet-tree multinomial regression model for associating dietary nutrients with gut microorganisms.\",\"citation\":\"Wang T, Zhao H. A Dirichlet-tree multinomial regression model for associating dietary nutrients with gut microorganisms. Biometrics 2017, 73:792-801.\",\"year\":2017,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/28112797\",\"text\":\"A Dirichlet-tree multinomial regression model for associating dietary nutrients with gut microorganisms.\"}},{\"title\":\"A Powerful Approach to Estimating Annotation-Stratified Genetic Covariance via GWAS Summary Statistics.\",\"citation\":\"Lu Q, Li B, Ou D, Erlendsdottir M, Powles RL, Jiang T, Hu Y, Chang D, Jin C, Dai W, He Q, Liu Z, Mukherjee S, Crane PK, Zhao H. A Powerful Approach to Estimating Annotation-Stratified Genetic Covariance via GWAS Summary Statistics. American Journal Of Human Genetics 2017, 101:939-964.\",\"year\":2017,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/29220677\",\"text\":\"A Powerful Approach to Estimating Annotation-Stratified Genetic Covariance via GWAS Summary Statistics.\"}},{\"title\":\"Joint modeling of genetically correlated diseases and functional annotations increases accuracy of polygenic risk prediction.\",\"citation\":\"Hu Y, Lu Q, Liu W, Zhang Y, Li M, Zhao H. Joint modeling of genetically correlated diseases and functional annotations increases accuracy of polygenic risk prediction. PLoS Genetics 2017, 13:e1006836.\",\"year\":2017,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/28598966\",\"text\":\"Joint modeling of genetically correlated diseases and functional annotations increases accuracy of polygenic risk prediction.\"}},{\"title\":\"On joint estimation of Gaussian graphical models for spatial and temporal data.\",\"citation\":\"Lin Z, Wang T, Yang C, Zhao H. On joint estimation of Gaussian graphical models for spatial and temporal data. Biometrics 2017, 73:769-779.\",\"year\":2017,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/28099997\",\"text\":\"On joint estimation of Gaussian graphical models for spatial and temporal data.\"}},{\"title\":\"On high-dimensional misspecified mixed model analysis in genome-wide association study\",\"citation\":\"J. Jiang, C. Li, D. Paul, C. Yang, H. Zhao (2016) On high-dimensional misspecified mixed model analysis in genome-wide association study. Annals of Statistics, 44: 2127–2160.\",\"year\":2016,\"link\":null},{\"title\":\"Integrative Tissue-Specific Functional Annotations in the Human Genome Provide Novel Insights on Many Complex Traits and Improve Signal Prioritization in Genome Wide Association Studies.\",\"citation\":\"Lu Q, Powles RL, Wang Q, He BJ, Zhao H. Integrative Tissue-Specific Functional Annotations in the Human Genome Provide Novel Insights on Many Complex Traits and Improve Signal Prioritization in Genome Wide Association Studies. PLoS Genetics 2016, 12:e1005947.\",\"year\":2016,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/27058395\",\"text\":\"Integrative Tissue-Specific Functional Annotations in the Human Genome Provide Novel Insights on Many Complex Traits and Improve Signal Prioritization in Genome Wide Association Studies.\"}},{\"title\":\"CCor: A whole genome network-based similarity measure between two genes.\",\"citation\":\"Hu Y, Zhao H. CCor: A whole genome network-based similarity measure between two genes. Biometrics 2016, 72:1216-1225.\",\"year\":2016,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/26953524\",\"text\":\"CCor: A whole genome network-based similarity measure between two genes.\"}},{\"title\":\"On an additive partial correlation operator and nonparametric estimation of graphical models.\",\"citation\":\"Lee KY, Li B, Zhao H. On an additive partial correlation operator and nonparametric estimation of graphical models. Biometrika 2016, 103:513-530.\",\"year\":2016,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/29422689\",\"text\":\"On an additive partial correlation operator and nonparametric estimation of graphical models.\"}},{\"title\":\"Pervasive pleiotropy between psychiatric disorders and immune disorders revealed by integrative analysis of multiple GWAS.\",\"citation\":\"Wang Q, Yang C, Gelernter J, Zhao H. Pervasive pleiotropy between psychiatric disorders and immune disorders revealed by integrative analysis of multiple GWAS. Human Genetics 2015, 134:1195-209.\",\"year\":2015,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/26340901\",\"text\":\"Pervasive pleiotropy between psychiatric disorders and immune disorders revealed by integrative analysis of multiple GWAS.\"}},{\"title\":\"A MARKOV RANDOM FIELD-BASED APPROACH TO CHARACTERIZING HUMAN BRAIN DEVELOPMENT USING SPATIAL-TEMPORAL TRANSCRIPTOME DATA.\",\"citation\":\"Lin Z, Sanders SJ, Li M, Sestan N, State MW, Zhao H. A MARKOV RANDOM FIELD-BASED APPROACH TO CHARACTERIZING HUMAN BRAIN DEVELOPMENT USING SPATIAL-TEMPORAL TRANSCRIPTOME DATA. The Annals Of Applied Statistics 2015, 9:429-451.\",\"year\":2015,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/26877824\",\"text\":\"A MARKOV RANDOM FIELD-BASED APPROACH TO CHARACTERIZING HUMAN BRAIN DEVELOPMENT USING SPATIAL-TEMPORAL TRANSCRIPTOME DATA.\"}},{\"title\":\"A statistical framework to predict functional non-coding regions in the human genome through integrated analysis of annotation data.\",\"citation\":\"Lu Q, Hu Y, Sun J, Cheng Y, Cheung KH, Zhao H. A statistical framework to predict functional non-coding regions in the human genome through integrated analysis of annotation data. Scientific Reports 2015, 5:10576.\",\"year\":2015,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/26015273\",\"text\":\"A statistical framework to predict functional non-coding regions in the human genome through integrated analysis of annotation data.\"}},{\"title\":\"GPA: a statistical approach to prioritizing GWAS results by integrating pleiotropy and annotation.\",\"citation\":\"Chung D, Yang C, Li C, Gelernter J, Zhao H. GPA: a statistical approach to prioritizing GWAS results by integrating pleiotropy and annotation. PLoS Genetics 2014, 10:e1004787.\",\"year\":2014,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/25393678\",\"text\":\"GPA: a statistical approach to prioritizing GWAS results by integrating pleiotropy and annotation.\"}},{\"title\":\"On an Additive Semigraphoid Model for Statistical Networks With Application to Pathway Analysis.\",\"citation\":\"Li B, Chun H, Zhao H. On an Additive Semigraphoid Model for Statistical Networks With Application to Pathway Analysis. Journal Of The American Statistical Association 2014, 109:1188-1204.\",\"year\":2014,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/26401064\",\"text\":\"On an Additive Semigraphoid Model for Statistical Networks With Application to Pathway Analysis.\"}},{\"title\":\"Improving genetic risk prediction by leveraging pleiotropy.\",\"citation\":\"Li C, Yang C, Gelernter J, Zhao H. Improving genetic risk prediction by leveraging pleiotropy. Human Genetics 2014, 133:639-50.\",\"year\":2014,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/24337655\",\"text\":\"Improving genetic risk prediction by leveraging pleiotropy.\"}},{\"title\":\"Guilt by rewiring: gene prioritization through network rewiring in genome wide association studies.\",\"citation\":\"Hou L, Chen M, Zhang CK, Cho J, Zhao H. Guilt by rewiring: gene prioritization through network rewiring in genome wide association studies. Human Molecular Genetics 2014, 23:2780-90.\",\"year\":2014,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/24381306\",\"text\":\"Guilt by rewiring: gene prioritization through network rewiring in genome wide association studies.\"}},{\"title\":\"Sparse principal component analysis by choice of norm.\",\"citation\":\"Qi X, Luo R, Zhao H. Sparse principal component analysis by choice of norm. Journal Of Multivariate Analysis 2013, 114:127-160.\",\"year\":2013,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/23524453\",\"text\":\"Sparse principal component analysis by choice of norm.\"}},{\"title\":\"Sparse Estimation of Conditional Graphical Models With Application to Gene Networks.\",\"citation\":\"Li B, Chuns H, Zhao H. Sparse Estimation of Conditional Graphical Models With Application to Gene Networks. Journal Of The American Statistical Association 2012, 107:152-167.\",\"year\":2012,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/24574574\",\"text\":\"Sparse Estimation of Conditional Graphical Models With Application to Gene Networks.\"}},{\"title\":\"iFad: an integrative factor analysis model for drug-pathway association inference.\",\"citation\":\"Ma H, Zhao H. iFad: an integrative factor analysis model for drug-pathway association inference. Bioinformatics (Oxford, England) 2012, 28:1911-8.\",\"year\":2012,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/22581178\",\"text\":\"iFad: an integrative factor analysis model for drug-pathway association inference.\"}},{\"title\":\"Incorporating biological pathways via a Markov random field model in genome-wide association studies.\",\"citation\":\"Chen M, Cho J, Zhao H. Incorporating biological pathways via a Markov random field model in genome-wide association studies. PLoS Genetics 2011, 7:e1001353.\",\"year\":2011,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/21490723\",\"text\":\"Incorporating biological pathways via a Markov random field model in genome-wide association studies.\"}},{\"title\":\"BAYESIAN HIERARCHICAL MODELING FOR SIGNALING PATHWAY INFERENCE FROM SINGLE CELL INTERVENTIONAL DATA.\",\"citation\":\"Luo R, Zhao H. BAYESIAN HIERARCHICAL MODELING FOR SIGNALING PATHWAY INFERENCE FROM SINGLE CELL INTERVENTIONAL DATA. The Annals Of Applied Statistics 2011, 5:725-745.\",\"year\":2011,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/22162986\",\"text\":\"BAYESIAN HIERARCHICAL MODELING FOR SIGNALING PATHWAY INFERENCE FROM SINGLE CELL INTERVENTIONAL DATA.\"}}],\"clinicalTrials\":[{\"url\":\"/ycci/trial/1246/\",\"title\":\"Pathogenesis of Youth and Type 2 Diabetes and Prediabetes\",\"conditions\":[\"Children's Health\",\"Diabetes Mellitus - Type 2\",\"Diseases of the Endocrine System\"]}],\"coauthors\":[]},\"patientCareTab\":null,\"newsTab\":{\"searchUrl\":\"/ysm/search/?entityType=Article&profilePageName=hongyu_zhao\",\"news\":[{\"id\":\"28169\",\"bylineDate\":\"2020-10-21T04:00:00-04:00\",\"title\":\"New Insights on Genetic Risks for Cigarette Smoking\",\"summary\":\"Scientists have long understood that genes play an important role in a person’s smoking behavior, and a new genome-wide association study by Yale and U.S. Department of Veterans Affairs (VA) researchers is helping to explain why some people start to smoke, while others are able to quit.\",\"thumbnail\":{\"url\":\"https://ysm-res.cloudinary.com/image/upload/v1/yms/prod/b9a448ed-b277-4544-9963-d1c9125db54f\",\"altText\":null,\"width\":2862,\"height\":4106,\"imageVariants\":[{\"dimension\":\"Rectangle\",\"url\":\"https://ysm-res.cloudinary.com/image/upload/v1/yms/prod/c40abbbd-2891-4647-a7fa-a4c79032e6bc\",\"width\":2861,\"height\":1536},{\"dimension\":\"Square\",\"url\":\"https://ysm-res.cloudinary.com/image/upload/v1/yms/prod/95dc8582-f1ee-4a41-8520-8070d6c88b6e\",\"width\":2862,\"height\":2862}]},\"url\":\"/ysm/news-article/28169/\",\"isFeatured\":false,\"source\":null},{\"id\":\"25679\",\"bylineDate\":\"2020-06-30T04:00:00-04:00\",\"title\":\"Senior YSPH Professors Retiring\",\"summary\":\"Three senior professors who made substantial contributions to their scientific fields and provided many years of service and leadership at the Yale School of Public Health are retiring on July 1.\",\"thumbnail\":{\"url\":\"https://ysm-res.cloudinary.com/image/upload/v1/yms/prod/7bed4423-0a4f-4ab6-a958-6bcfd6af2a2c\",\"altText\":null,\"width\":2100,\"height\":1500,\"imageVariants\":[{\"dimension\":\"Square\",\"url\":\"https://ysm-res.cloudinary.com/image/upload/v1/yms/prod/14415fe0-82ca-4db8-86e2-0458fb33ad16\",\"width\":1500,\"height\":1500},{\"dimension\":\"Rectangle\",\"url\":\"https://ysm-res.cloudinary.com/image/upload/v1/yms/prod/f17575f5-d34b-46c0-acf1-48443c056adc\",\"width\":2041,\"height\":1096}]},\"url\":\"/ysm/news-article/25679/\",\"isFeatured\":false,\"source\":null},{\"id\":\"25364\",\"bylineDate\":\"2020-06-12T04:00:00-04:00\",\"title\":\"Bilateral Forum on International Public Health Policy Research Directions between Yale and Shanghai Jiao Tong University\",\"summary\":\"A Bilateral Forum on International Public Health Policy Research Directions was held on May 23, 2020. The forum took place at Shanghai Jiao Tong University (SJTU) and included Yale participants by videoconference. Paul D. Cleary, the Anna M.R. Lauder Professor of Health Policy at the Yale School of Public Health and Director of SJTU-Yale Joint Center for Health Policy delivered closing remarks. He drew attention to the importance of increasing information exchanges between countries to jointly fight the COVID-19 pandemic as well as prepare for future global public health challenges. He noted that “sharing information openly about how infectious diseases spread as well as the strengths and limitations of different containment and mitigation strategies will be essential for more effectively addressing future outbreaks, as well as for continuing to address the current pandemic.”\",\"thumbnail\":{\"url\":\"https://ysm-res.cloudinary.com/image/upload/v1/yms/prod/7cbb39e4-3474-4002-93c1-d68822a70a35\",\"altText\":null,\"width\":1220,\"height\":850,\"imageVariants\":[{\"dimension\":\"Square\",\"url\":\"https://ysm-res.cloudinary.com/image/upload/v1/yms/prod/1132f996-6e19-4282-ac47-014abcfcc4c1\",\"width\":735,\"height\":735},{\"dimension\":\"Rectangle\",\"url\":\"https://ysm-res.cloudinary.com/image/upload/v1/yms/prod/9003fc95-81b8-47b4-abf1-acd71af20142\",\"width\":1220,\"height\":655}]},\"url\":\"https://world.yale.edu/news/bilateral-forum-international-public-health-policy\",\"isFeatured\":false,\"source\":\"Yale and the 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action.\",\"thumbnail\":{\"url\":\"https://ysm-res.cloudinary.com/image/upload/v1/yms/prod/287e5508-3ffa-4f49-8b7c-f481920f71ea\",\"altText\":null,\"width\":2100,\"height\":1028,\"imageVariants\":[{\"dimension\":\"Square\",\"url\":\"https://ysm-res.cloudinary.com/image/upload/v1/yms/prod/6aab3e14-9fb9-4e77-aa59-86a1901ea2c9\",\"width\":1028,\"height\":1028},{\"dimension\":\"Rectangle\",\"url\":\"https://ysm-res.cloudinary.com/image/upload/v1/yms/prod/2e2e6f78-eb82-49bb-b749-ece4b9c2c0df\",\"width\":1914,\"height\":1028}]},\"url\":\"/ysm/news-article/24812/\",\"isFeatured\":false,\"source\":null},{\"id\":\"22951\",\"bylineDate\":\"2020-03-04T21:38:32-05:00\",\"title\":\"Geneticist Sidi Chen Receives Large Award Which Will Further His Breast Cancer Work\",\"summary\":\"This Department of Defense award goes to the “best and brightest in their fields,” and recognizes “creative and innovative individuals.” Investigators are chosen for their ability “to go beyond conventional thinking” in 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Incorporating biological pathways via a Markov random field model in genome-wide association studies. PLoS Genetics 2011, 7:e1001353.\\\",\\\"@context\\\":\\\"http://schema.org\\\"}\"},{\"schemaName\":\"MedicalScholarlyArticle\",\"jsonData\":\"{\\\"@type\\\":\\\"MedicalScholarlyArticle\\\",\\\"publicationType\\\":\\\"Journal Article\\\",\\\"inLanguage\\\":\\\"en-US\\\",\\\"headline\\\":\\\"BAYESIAN HIERARCHICAL MODELING FOR SIGNALING PATHWAY INFERENCE FROM SINGLE CELL INTERVENTIONAL DATA.\\\",\\\"url\\\":\\\"https://www.ncbi.nlm.nih.gov/pubmed/22162986\\\",\\\"author\\\":[{\\\"@type\\\":\\\"Person\\\",\\\"mainEntityOfPage\\\":{\\\"@type\\\":\\\"WebPage\\\",\\\"@id\\\":\\\"https://publichealth.yale.edu/profile/hongyu_zhao/\\\",\\\"@context\\\":\\\"http://schema.org\\\"},\\\"name\\\":\\\"Hongyu Zhao\\\",\\\"description\\\":\\\"Dr. Hongyu Zhao is the Ira V. Hiscock Professor of Biostatistics and Professor of Statistics and Data Science and Genetics, Chair of the Biostatistics Department and the Co-Director of Graduate Studies of the Inter-Departmental Program in Computational Biology and Bioinformatics at Yale University. He received his B.S. in Probability and Statistics from Peking University in 1990 and Ph.D. in Statistics from the University of California at Berkeley in 1995. His research interests are the developments and applications of novel statistical methods to address scientific questions in genetics, molecular biology, drug developments, and precision medicine.Some of his recent projects include large scale genome wide studies to identify genetic variants underlying complex diseases, genetic risk prediction, single cell analysis, biological network modeling and analysis, disease biomarker identification, genome annotation, cancer genomics, microbiome analysis, and image analysis. 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