Penn State Biomedical Big Data to Knowledge (B2D2K) Training Program

宾夕法尼亚州立大学生物医学大数据知识（B2D2K）培训计划

• 批准号: 9979949
• 负责人: JAMES R. BROACH
• 金额: $ 28.86万
• 依托单位: PENNSYLVANIA STATE UNIVERSITY, THE
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-04-01 至 2021-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9979949
• 关键词: Big Data to Knowledge; Training Programs; big biomedical data

 DESCRIPTION (provided by applicant): The BD2K initiative was developed by the NIH to enable biomedical researchers to capitalize on the Big Data being generated, foster new discovery and increase biological knowledge. The need to train a new generation of skilled scientists in computation, informatics, and statistics to surmount the challenges of big data analysis for biological and biomedical science is widely recognized. An important recommendation with respect to big data computing was to "build capacity by training the workforce in the relevant quantitative sciences such as bioinformatics, biomathematics, biostatistics, and clinical informatics". Basic science and biomedical advances rely increasingly on these very large, complex datasets generated by high throughput -omic and other biological technologies, and sound statistical reasoning and sophisticated computational techniques are needed throughout the process of analysis and discovery. This includes all stages of investigation, from experimental design and data pre-processing, de-noising and normalization, to integrating multiple datasets, testing hypotheses, and visualizing data in interactive and informative ways. The new challenges posed by high dimensional and complex data require that life and computer scientists working with big data acquire a substantive understanding of statistics and bioinformatics, and that statisticians working in this area, in return, acquire a substantive understanding of biological principles, experimental technologies and computation. These will converge into an interdisciplinary domain where existing statistical and computational tools are used and combined effectively, and novel methods are generated, to promote innovation and discovery in big data analysis for biomedical science. This interdisciplinary communication is essential for the emergence of a new cadre of researchers who can effectively communicate with their peers in the complementary disciplines required for tackling real problems important for life sciences in big data. The Biomedical Big Data to Knowledge (B2D2K) Training Program at The Pennsylvania State University will bring together Data Science researchers and educators from 5 colleges at Penn State: the Colleges of Science, Engineering, Health and Human Development, Information Sciences and Technology, and Medicine, and Geisinger Health System to create a truly transformative multi-disciplinary predoctoral training environment. The goal of the B2D2K program is to train a diverse cohort comprising the next-generation biomedical data scientists with a deep knowledge of Data Science to develop novel algorithmic and statistical methods for building predictive, explanatory, and causal models through integrative analyses of disparate types of biomedical data (including Electronic Health Records, genomics, behavioral, socio-economic, and environmental data) to advance science and improve health. We believe that the investment in this generation of data scientists will be critical to see all of the `Biomedical Big Data' fully utilized to its greatest potential.

 BD 2K计划由NIH开发，旨在使生物医学研究人员能够利用正在生成的大数据，促进新发现并增加生物学知识。人们普遍认识到，需要培养新一代计算、信息学和统计学方面的熟练科学家，以克服生物和生物医学科学大数据分析的挑战。关于大数据计算的一项重要建议是“通过培训相关定量科学，如生物信息学、生物数学、生物统计学和临床信息学方面的工作人员来建设能力”。基础科学和生物医学的进步越来越依赖于高通量-组学和其他生物技术产生的这些非常大的复杂数据集，并且在整个分析和发现过程中需要合理的统计推理和复杂的计算技术。这包括调查的所有阶段，从实验设计和数据预处理，去噪和归一化，到整合多个数据集，测试假设，以及以交互和信息方式可视化数据。高维和复杂数据带来的新挑战要求从事大数据工作的生命科学家和计算机科学家对统计和生物信息学有实质性的了解，而从事这一领域工作的统计学家反过来也对生物学原理、实验技术和计算有实质性的了解。这些将汇聚成一个跨学科领域，有效地使用和结合现有的统计和计算工具，并产生新的方法，以促进生物医学科学大数据分析的创新和发现。这种跨学科交流对于新的研究人员队伍的出现至关重要，他们可以在解决大数据中生命科学重要的真实的问题所需的互补学科中与同行进行有效沟通。宾夕法尼亚州立大学的生物医学大数据到知识（B2 D2 K）培训计划将汇集来自宾夕法尼亚州立大学5所学院的数据科学研究人员和教育工作者：科学学院，工程学院，健康与人类发展学院，信息科学与技术学院，医学学院，以及Geisinger Health System，以创建一个真正变革的多学科博士前培训环境。B2 D2 K计划的目标是培养一个多元化的群体，包括具有数据科学深厚知识的下一代生物医学数据科学家，以开发新的算法和统计方法，通过对不同类型的生物医学数据的综合分析来构建预测，解释和因果模型。（包括电子健康记录，基因组学，行为，社会经济和环境数据），以推进科学和改善健康。我们相信，对这一代数据科学家的投资对于看到所有“生物医学大数据”充分发挥其最大潜力至关重要。

项目成果

Simultaneous feature selection and outlier detection with optimality guarantees.

• DOI: 10.1111/biom.13553
• 发表时间: 2022-12
• 期刊: BIOMETRICS
• 影响因子: 1.9
• 作者: Insolia, Luca;Kenney, Ana;Chiaromonte, Francesca;Felici, Giovanni
• 通讯作者: Felici, Giovanni

Gut microbiota and intestinal FXR mediate the clinical benefits of metformin.

肠道微生物群和肠道 FXR 介导二甲双胍的临床益处。

• DOI: 10.1038/s41591-018-0222-4
• 发表时间: 2018-12
• 期刊: Nature medicine
• 影响因子: 82.9
• 作者: Sun L;Xie C;Wang G;Wu Y;Wu Q;Wang X;Liu J;Deng Y;Xia J;Chen B;Zhang S;Yun C;Lian G;Zhang X;Zhang H;Bisson WH;Shi J;Gao X;Ge P;Liu C;Krausz KW;Nichols RG;Cai J;Rimal B;Patterson AD;Wang X;Gonzalez FJ;Jiang C
• 通讯作者: Jiang C