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DTSTART;TZID=America/New_York:20220204T130000
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SUMMARY:Data Science Seminar: Deciphering Tissue Microenvironment from Next Generation Sequencing Data
DESCRIPTION:Friday February 4\, 2022\n1:00PM Eastern Time \nRegister. \nJian Hu\nPhD Candidate\, Department of Biostatistics\, Epidemiology and Informatics\nUniversity of Pennsylvania \nABSTRACT: The advent of high-throughput next-generation sequencing (NGS) technologies has transformed our understanding of cell biology and human disease. As NGS has been adopted earliest by the scientific community\, its use has now become widespread\, and the technology has improved rapidly. At present\, it is now common for laboratories to assay genome-wide transcriptomes of thousands of cells in a single scRNA-seq experiment. In addition\, technologies that enable the measurement of new information\, for example\, chromatin accessibility\, protein quantification\, and spatial location\, have been developed. In order to take full advantage of the multi-modality information when analyzing NGS data\, new methods are demanded. This seminar will introduce several machine learning algorithms for NGS data analysis with different aims\, including cell type classification\, spatial domain detection\, and tumor microenvironment annotation. \nParts of the talk are based on the following two papers: \n\nhttps://www.nature.com/articles/s42256-020-00233-7 \n\nhttps://www.nature.com/articles/s41592-021-01255-8 \nKEYWORDS: single cell RNA sequencing (scRNA-seq)\, Spatial transcriptomics (ST)\, tumor microenvironment\, machine learning
URL:https://ds.dfci.harvard.edu/event/data-science-seminar-deciphering-tissue-microenvironment-from-next-generation-sequencing-data/
CATEGORIES:Seminar
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220208T130000
DTEND;TZID=America/New_York:20220208T140000
DTSTAMP:20260422T014232
CREATED:20210614T165138Z
LAST-MODIFIED:20220215T201001Z
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SUMMARY:Frontiers in Biostatistics: Early Phase Design Considerations for Oncology Drug Development in the Era of Immunotherapy and Targeted Agents
DESCRIPTION:Tuesday\, February 8\, 2022\n1:00pm Eastern Time \nYouTube Video \nElizabeth Garrett-Mayer\, PhD\, FSCT\nVice President\nCenter for Research and Analytics (CENTRA) \nDr. Garrett-Mayer joined ASCO in 2017 as CENTRA’s Division Director for Biostatistics and Research Data Governance and became CENTRA’s first Vice President in 2022. CENTRA leads ASCO’s research efforts\, including the TAPUR Study\, ASCO’s COVID-19 Registry\, and research projects aimed at increasing minority enrollment and expanding eligibility criteria in clinical trials. Prior to joining ASCO\, she served on the faculty in the Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins in the Department of Oncology\, and then joined the faculty of the Medical University of South Carolina (MUSC) and established the Biostatistics Shared Resource at the Hollings Cancer Center (HCC). \nShe earned her bachelor’s degree from Bowdoin College in Math and Economics\, and a PhD in Biostatistics from Johns Hopkins Bloomberg School of Public Health. Her publication record includes more than 280 peer-reviewed publications\, primarily in cancer research and research methods. She has also been a member of numerous NIH grant review committees\, Data Safety Monitoring Boards for NIH-supported clinical trials and has served on the editorial board of three peer-reviewed journals (Clinical Trials\, Cancer\, and the Journal of the National Cancer Institute)\, and as faculty on the ASCO/AACR Methods in Clinical Cancer Research Workshop for over a decade. \n  \nAbstract: \nUncertainty about optimality of doses of anti-cancer agents approved in recent years\, including PD-L1 blockade agents and targeted agents\, has led to a movement in the oncology drug development field to reconsider the traditional approach for drug development.  Following in the path of approvals for nivolumab and pembrolizumab\, trial designs for dose finding trials have ballooned from sample sizes in the range of 12-50 to the hundreds or more.  In addition\, post-marketing trials have demonstrated that lower doses of certain drugs may be as efficacious as approved doses.  The has contributed to a renewed focus on ‘dose optimization\,’ causing statisticians\, researchers\, patient advocates and regulators to realize the profound inadequacies of traditional phase I dose finding designs. Trial approaches need to incorporate the adaptive nature of a seamless toxicity-efficacy paradigm\, while maintaining practical aspects of trial implementation\, statistical properties\, endpoint measurement and safety and well-being of patients. This talk will discuss these aspects and provide recent context of drug approvals in oncology.
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-elizabeth-garrett-meyer/
CATEGORIES:Seminar
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DTSTART;TZID=America/New_York:20220215T130000
DTEND;TZID=America/New_York:20220215T140000
DTSTAMP:20260422T014232
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SUMMARY:Data Science Seminar: End-to-end AI for Screening Mammography
DESCRIPTION:Tuesday February 15\, 2022\n1:00PM Eastern Time \nWilliam Lotter\, PhD\nVice President of Machine Learning\, RadNet\, Inc.\nChief Technology Officer & Co-Founder\, DeepHealth\, Inc. \nRegister. \nScreening mammography has been estimated to reduce breast cancer mortality by 20-40%\, but significant opportunities remain for improving access and overall quality. Artificial intelligence (AI) has the potential to deliver these improvements\, but developing clinically-effective AI presents additional challenges spanning development through clinical integration. In this talk\, I will present an AI algorithm that addresses the “needle-in-a-haystack” nature of mammography while efficiently using labeled training data and enabling localization-based explainability. I will then detail the algorithm’s performance\, providing evidence of generalization across populations and an ability to aid in earlier cancer detection. Finally\, I will discuss our efforts in achieving regulatory clearance and large-scale clinical deployment.
URL:https://ds.dfci.harvard.edu/event/data-science-seminar-end-to-end-ai-for-screening-mammography/
CATEGORIES:Seminar
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220224T130000
DTEND;TZID=America/New_York:20220224T140000
DTSTAMP:20260422T014232
CREATED:20220218T134643Z
LAST-MODIFIED:20220218T175729Z
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SUMMARY:Data Science Seminar: Spatial meshing for general Bayesian multivariate models
DESCRIPTION:Thursday February 24\, 2022\n1:00PM Eastern Time \nMichele Peruzzi\, PhD\nPostdoctoral Associate\, Department of Statistical Science\, Duke University \nRegister. \nAbstract: In this talk\, I will consider the problem of fitting Bayesian models with spatial random effects to large scale multivariate multi-type data from satellite imaging\, land-based weather and air quality sensors\, and citizen science\, with diverse applications in the environmental sciences\, ecology\, and public health. \nIn these contexts\, the goal of quantifying spatial associations via random effects in Bayesian hierarchical models can be achieved by letting a Gaussian process (GP) characterize dependence in space\, time\, and across outcomes. GPs have desirable properties and lead to extremely flexible models\, which are able to accurately quantify uncertainties. Unfortunately\, GPs are notoriously poor performers in large data settings. While the literature on scalable GPs has primarily focused on their well-known bottlenecks in models of univariate continuous outcomes\, I consider the more challenging hurdles to efficient computations facing latent models of multivariate non-Gaussian outcomes.\nI introduce spatial meshing and manifold preconditioning as tools for efficient computations of multivariate Bayesian models of spatially referenced non-Gaussian data. First\, I outline spatial meshing as a tool for building scalable processes using patterned directed acyclic graphs on partitioned spatial domains. Then\, I present manifold preconditioning as a novel Langevin method for superior sampling performance with non-Gaussian multivariate data that are common in studying species’ communities. \nIn addition to these main topics\, I discuss additional strategies for improving Markov-chain Monte Carlo performance\, concluding with applications showcasing the flexibility of the proposed methodologies. All presented methods are implemented in the high performance R package ‘meshed’\, available on CRAN.
URL:https://ds.dfci.harvard.edu/event/data-science-seminar-spatial-meshing-for-general-bayesian-multivariate-models/
CATEGORIES:Seminar
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220228T130000
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SUMMARY:Data Science Seminar: From descriptive to predictive biology via single-cell multiomics
DESCRIPTION:Monday February 28\, 2022\n1:00PM Eastern Time \nGenevieve Stein-O’Brien\nInstructor\, Johns Hopkins University\, School of Medicine\nDepartment of Oncology\, Division of Biostatistics and Bioinformatics;\nDepartment of Neuroscience; and McKusick-Nathans Department of Genomic Medicine\nAssistant Director\, Johns Hopkins University Single Cell Consortium \nRegister. \nAbstract: As the single-cell field races to characterize each cell type\, state\, and behavior\, the complexity of the computational analysis approaches the complexity of the biological systems. Single cell and imaging technologies now enable unprecedented measurements of state transitions in biological systems\, providing high-throughput data that capture tens-of-thousands of measurements on hundreds-of-thousands of samples. Thus\, the definition of cell type and state is evolving to encompass the broad range of biological questions now attainable. To answer these questions requires the development of computational tools for integrated multiomics analysis. Merged with statistical and mathematical models\, these algorithms will be able to forecast future states of biological systems\, going from statistical inferences of phenotypes to time course predictions of the biological systems with dynamic maps. Thus\, systems biology for forecasting biological system dynamics from multiomic data represents the future of cell biology empowering a new generation of technology-driven predictive medicine.
URL:https://ds.dfci.harvard.edu/event/data-science-seminar-from-descriptive-to-predictive-biology-via-single-cell-multiomics/
CATEGORIES:Seminar
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