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: 10039150
• 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/10039150
• 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; 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.

基于贝叶斯学习的大数据预测系统发育