Big Data Predictive Phylogenetics with Bayesian Learning

使用贝叶斯学习的大数据预测系统发育学

• 批准号: 10176406
• 负责人: Andrew James Holbrook
• 金额: $ 10.65万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10176406
• 关键词: Accounting; Address; Adoption; Air; Alzheimer' s Disease; Americas; Award; Bayesian Modeling; Bayesian learning; Behavior; Big Data; Biological; Biology; Collaborations; Complex; Computer Models; Computer software; Computing Methodologies; Dangerousness; Data; Data Analyses; Data Science; Development; Development Plans; Diffusion; Disease Outbreaks; Doctor of Medicine; Doctor of Philosophy; Earthquakes; Epidemic; Epidemiologist; Epidemiology; Evaluation; Event; Evolution; Faculty; Failure; Free Will; Generations; Geography; Goals; Health; Herd Immunity; High Performance Computing; Human Genetics; Individual; Influenza; Institutes; Investigation; Joints; Knowledge; Lead; Learning; Mathematics; Medical; Mentors; Mentorship; Methodology; Modeling; Pattern; Performance; Phylogenetic Analysis; Phylogeny; Process; Publications; Publishing; Recording of previous events; Research; Route; Schedule; Scientist; Ships; Speed; Statistical Computing; Stochastic Processes; Structure; Techniques; Testing; Time; Travel; Viral; Viral Epidemiology; Viral Physiology; Work; ZIKA; Zika Virus; blind; career development; epidemiological model; flexibility; innovation; insight; meetings; novel; open source; outbreak prediction; parallel computer; pathogen; reconstruction; response; statistics; transmission process

Big Data Predictive Phylogenetics with Bayesian Learning Abstract Andrew Holbrook, Ph.D., is a Bayesian statistician with a broad background in applied, theoretical and compu- tational data science. His proposed research Big Data Predictive Phylogenetics with Bayesian Learning tackles viral outbreak forecasting by combining Bayesian phylogenetic modeling with flexible, `self-exciting' stochastic process models. The development and publication of open-source, high-performance computing software for his models will facilitate fast epidemiological field response in a big data setting. Dr. Holbrook will apply his method- ology to the reconstruction of the 2015-2016 Zika virus epidemic in the Americas, focusing on identifying key geographical routes of transmission and phylogenetic clades with enhanced infectiousness. Candidate: Dr. Holbrook is Postdoctoral Scholar at the UCLA Department of Human Genetics. He earned his Ph.D. in Statistics from the Department of Statistics at UC Irvine, during which time he completed his dissertation Geometric Bayes, an investigation into Bayesian modeling and computing on abstract mathematical spaces, and simultaneously participated in scientific collaborations at the UC Irvine Alzheimer's Disease Research Center. The proposed career development plan will establish Dr. Holbrook as an independent leader in data intensive viral epidemiology by 1) facilitating coursework to build biological domain knowledge, 2) affording Dr. Holbrook the opportunity to lead his own project while remaining under the expert oversight of UCLA Prof. Marc Suchard, M.D., Ph.D., and 3) allowing Dr. Holbrook to continue his focus on quantitative viral epidemiology once he has moved to a faculty commitment. Mentors: During the first three years of the award period, Dr. Holbrook will work closely with Prof. Suchard, continuing their current schedule of weekly meetings. Prof. Suchard is a leading expert in both Bayesian phylo- genetics and high-performance statistical computing; and with his medical background, Prof. Suchard will advise Dr. Holbrook in his expansion of domain knowledge in viral epidemiology. As secondary mentor, Prof. Kristian Andersen, Ph.D., of the Scripps Institute will advise Dr. Holbrook in the impactful application of his statistical and computational methodologies to the 2015-2016 Zika virus epidemic. Dr. Holbrook and Profs. Suchard and Andersen will maintain their collaborations after the postdoctoral period. Research: Bayesian phylogenetics successfully reconstructs evolutionary histories but fails to predict viral spread. Self-exciting point processes are devoid of biological insight and fail to account for geographic networks of diffusion. Aim 1 addresses deficiencies in these two complementary viral epidemiological modeling techniques by innovating a combined model where the phylogenetic and self-excitatory components support each other. Aim 2 makes widespread adoption a reality by publishing open-source, massively parallel computing software suitable for big data analysis. Aim 3 reconstructs the 2015-2016 Zika epidemic, learns key geographical routes of transmission and identifies phylogenetic clades with enhanced infectiousness.

贝叶斯学习的大数据预测系统发育学 抽象的 安德鲁·霍尔布鲁克（Andrew Holbrook）博士是一位贝叶斯统计学家 倾向数据科学。他提出的研究大数据预测系统发育学用贝叶斯学习铲球 通过将贝叶斯系统发育建模与灵活的“自我激发”随机结合起来，通过将贝叶斯系统发育建模结合起来预测病毒爆发预测 过程模型。开发和发布开源，高性能计算软件 模型将促进大数据设置中快速流行病学领域的响应。霍尔布鲁克博士将采用他的方法 - 对2015 - 2016年美洲寨卡病毒流行的重建的研究，重点是识别关键 具有增强感染性的传播和系统发育进化枝的地理途径。 候选人：Holbrook博士是加州大学洛杉矶分校人类遗传学系的博士后学者。他赢得了他的 博士UC Irvine统计局的统计数据，在此期间他完成了论文 几何贝叶斯，对抽象数学空间上的贝叶斯建模和计算的投资，以及 同样，在加州大学阿尔茨海默氏病研究中心参加科学合作。 拟议的职业发展计划将建立Holbrook博士为数据密集型的独立领导者 1）促进建立生物领域知识的课程的病毒流行病学，2）霍尔布鲁克博士 有机会在加州大学洛杉矶分校教授马克·萨德（Marc Suchard）的专家监督下领导自己的项目， 医学博士，博士学位和3）允许霍尔布鲁克博士继续关注定量病毒流行病学 转向教师的承诺。 导师：在颁奖期的第一年，霍尔布鲁克博士将与Suchard教授紧密合作， 继续他们当前的每周会议时间表。 Suchard教授是两位贝叶斯Phylo-的领先专家 遗传学和高性能统计计算；凭借他的医学背景，Suchard教授会建议 霍尔布鲁克博士在扩展病毒流行病学领域知识方面。作为次要导师，克里斯蒂安教授 Scripps Institute的Andersen博士将建议Holbrook博士在其统计数据中有影响力的应用 和2015 - 2016年寨卡病毒流行的计算方法。 Holbrook博士和教授。 Suchard和 安德森将在博士后之后维持他们的合作。 研究：贝叶斯系统发育学成功地重建了进化史，但无法预测病毒 传播。自我激发点过程没有生物学洞察力，无法说明地理网络 差异。 AIM 1解决了这两种完整病毒流行病学建模技术中的定义 通过创新系统发育和自我引起的组件相互支持的组合模型。 AIM 2通过发布开源，大规模并行计算软件，使广泛采用的采用成为现实 适用于大数据分析。 AIM 3重建2015-2016 Zika流行病，学习关键的地理路线 具有增强感染性的传播和识别系统发育进化枝。