Biostatistics and Data Science Core

生物统计学和数据科学核心

• 批准号: 10689687
• 负责人: Li Luo
• 金额: $ 11.81万
• 依托单位: UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-25 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10689687
• 关键词: Accounting; Air Pollutants; Basic Science; Bayesian Method; Bioinformatics; Bioinformatics Shared Resource; Biometry; Biostatistical Methods; Cardiovascular Diseases; Characteristics; Chemistry; Clinical Research; Collaborations; Communication; Communities; Community Health; Complex; Complex Analysis; Complex Mixtures; Comprehensive Cancer Center; Data; Data Analyses; Data Analytics; Data Collection; Data Science; Data Science Core; Data Security; Data Set; Databases; Development; Dimensions; Disease; Dose; Education; Educational process of instructing; Educational workshop; Ensure; Environment; Environmental Exposure; Environmental Health; Environmental Science; Etiology; FAIR principles; Faculty; Funding; Generations; Genetic; Genomics; Geographic Information Systems; Health; Infectious Agent; Infrastructure; Interdisciplinary Study; Intervention; Knowledge; Lead; Life Cycle Stages; Malignant Neoplasms; Manuscripts; Metal exposure; Methodology; Methods; Modeling; Modernization; Monitor; New Mexico; Nutrient; Outcome; Pathway interactions; Physical activity; Policy Making; Population Study; Positioning Attribute; Practice Management; Prevention strategy; Process; Protocols documentation; Publishing; Quality Control; Research; Research Activity; Research Design; Research Methodology; Research Personnel; Research Project Grants; Risk Assessment; Risk Factors; Role; Sample Size; Science; Scientist; Services; Statistical Data Interpretation; Surveys; Techniques; Testing; Training; Translational Research; United States National Institutes of Health; Universities; Visualization software; Work; analytical method; bioinformatics pipeline; bioinformatics tool; cancer genomics; citizen science; complex data; data hub; data infrastructure; data management; design; dietary; environmental chemical; epidemiology study; experience; exposure pathway; high dimensionality; improved; insight; large datasets; large scale data; lectures; member; metabolomics; method development; microbiome research; multidimensional data; multidisciplinary; novel; population based; programs; response; statistical and machine learning; tool; transcriptomics

Biostatistics and Data Science Core (BDSC) - Project Summary The objective of the BDSC is to provide state-of-the-art support in data management, high dimensional complex data science and bioinformatics, and cutting-edge biostatistical and geospatial analysis to meet the data management and analysis challenges of working with environmental exposure mixtures, and to advance the environmental health research performed by the NM-INSPIRES Center investigators. The BDSC will be co- led by Drs. Li Luo and Yan Guo. Dr. Luo has over 10 years of experience in high dimensional data analysis, environmental chemical mixture modeling, causal inference, genetic-environment interaction and integrative analysis, and statistical analysis of data from large scale population-based epidemiological and clinical studies. Dr. Luo has lead the data management and analysis team supporting multiple NIH-funded environmental health research projects and programs for over 3 years. Dr. Guo is a Bioinformatics expert who is the Director of the Bioinformatics Shared Resources at the University of New Mexico Comprehensive Cancer Center. Dr. Guo’s research has been focused on the development of bioinformatics methodology and analysis approaches for cancer genomic studies and has published more than 150 manuscripts in the related fields. Core members include eight quantitative scientists with complementary expertise in data management and analysis for a variety of scientific domains. Core members have backgrounds and expertise in a wide range of quantitative research methodologies, including modern FAIR compliant data management, biostatistical methods, bioinformatics tools, geospatial analysis and modeling, survey data analysis, Bayesian methods, and causal inference. The team members have collaborated for many years on multiple environmental health projects, and have streamlined the workflow from data collection, quality control, bioinformatics, biostatistical and geospatial analysis support, and methodology developments to enhance the analysis of environmental health data sets. The Core will serve as a hub for all research activities of the Center and provide a wide array of analytical support to Center investigators engaging in environmental health research. The BDSC support will contribute to many aspects of the research process including, but not limited to, efficient study design, appropriate monitoring of data security, enhanced data management capacity, state-of-the-art bioinformatics, biostatistical and geospatial analyses, assistance in the development of study protocols and proposals, as well as sample size and power calculations. Core faculty will also provide educational opportunities to Center investigators and trainees through lectures, classroom teaching, workshops, and hands-on training. The collaborative efforts of Core members will contribute not only to effectively pursuing hypothesis-driven research questions but also to developing novel research questions through complex analyses of large datasets. BDSC is well positioned to promote multidisciplinary and interdisciplinary research and develop analytical strategies to contribute to the successful completion of basic, community-based, and translational research projects within the Center.

生物统计和数据科学核心 (BDSC) - 项目摘要 BDSC 的目标是为数据管理、高维数据提供最先进的支持。 复杂的数据科学和生物信息学，以及尖端的生物统计和地理空间分析，以满足 处理环境暴露混合物的数据管理和分析挑战，并推进 NM-INSPIRES 中心研究人员进行的环境健康研究。 BDSC 将共同 由博士领导。黎洛和燕国。罗博士拥有超过10年的高维数据分析经验， 环境化学混合物建模、因果推理、遗传-环境相互作用和综合 对大规模人群流行病学和临床研究数据进行分析和统计分析。 罗博士领导的数据管理和分析团队支持多个NIH资助的环境项目 超过3年的健康研究项目和计划。郭博士是生物信息学专家，担任主任 新墨西哥大学综合癌症中心的生物信息学共享资源。博士。 郭的研究重点是生物信息学方法和分析方法的发展 致力于癌症基因组研究，并在相关领域发表了 150 多篇手稿。核心成员 包括八位定量科学家，他们在数据管理和分析方面具有互补的专业知识 各种科学领域。核心成员拥有广泛的量化背景和专业知识 研究方法，包括现代 FAIR 合规数据管理、生物统计方法、 生物信息学工具、地理空间分析和建模、调查数据分析、贝叶斯方法和因果关系 推理。团队成员在多个环境健康项目上合作多年，并且 简化了数据收集、质量控制、生物信息学、生物统计和地理空间的工作流程 分析支持和方法开发，以加强环境健康数据集的分析。 该核心将作为该中心所有研究活动的枢纽，并提供广泛的分析 支持中心研究人员从事环境健康研究。 BDSC 支持将做出贡献 研究过程的许多方面，包括但不限于有效的研究设计、适当的研究 监测数据安全、增强数据管理能力、最先进的生物信息学、生物统计 和地理空间分析，协助制定研究方案和建议以及样本 尺寸和功率计算。核心教师还将为中心研究人员和 学员通过讲座、课堂教学、研讨会和实践培训。各方的共同努力 核心成员不仅将为有效地追求假设驱动的研究问题做出贡献，而且还将为 通过对大型数据集的复杂分析来开发新的研究问题。 BDSC 处于有利地位 促进多学科和跨学科研究并制定分析策略，以促进 成功完成中心内的基础、社区和转化研究项目。