Data-driven science and diversity in the All of Us Research Program

被引:8
|
作者
Ginsburg, Geoffrey S. [1 ]
Denny, Joshua C. [1 ]
Schully, Sheri D. [1 ]
机构
[1] NIH, All Us Res Program, Bethesda, MD 20892 USA
关键词
D O I
10.1126/scitranslmed.ade9214
中图分类号
Q2 [细胞生物学];
学科分类号
071009 ; 090102 ;
摘要
The National Institutes of Health's All of Us Research Program is an accessible platform that hosts genomic and phenotypic data to be collected from 1 million participants in the United States. Its mission is to accelerate medical research and clinical breakthroughs with a special emphasis on diversity.
引用
收藏
页数:5
相关论文
共 50 条
  • [31] Data-Driven Supervised Learning for Life Science Data
    Muench, Maximilian
    Raab, Christoph
    Biehl, Michael
    Schleif, Frank-Michael
    FRONTIERS IN APPLIED MATHEMATICS AND STATISTICS, 2020, 6
  • [32] A Comparison of Hypothesis-Driven and Data-Driven Research A Case Study in Multimodal Data Science in Gut-Brain Axis Research
    Dreisbach, Caitlin
    Maki, Katherine
    CIN-COMPUTERS INFORMATICS NURSING, 2023, 41 (07) : 497 - 506
  • [33] Smart City Data Science: Towards data-driven smart cities with open research issues
    Sarker, Iqbal H.
    INTERNET OF THINGS, 2022, 19
  • [34] Data-Driven Detection of Recursive Program Schemes
    Hofmann, Martin
    Schmid, Ute
    ECAI 2010 - 19TH EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE, 2010, 215 : 1063 - +
  • [35] Data-driven chance constrained stochastic program
    Jiang, Ruiwei
    Guan, Yongpei
    MATHEMATICAL PROGRAMMING, 2016, 158 (1-2) : 291 - 327
  • [36] Data-driven chance constrained stochastic program
    Ruiwei Jiang
    Yongpei Guan
    Mathematical Programming, 2016, 158 : 291 - 327
  • [37] Big data-driven Biomedical research
    Hahn, Sun-Hwa
    2018 10TH INTERNATIONAL CONFERENCE ON KNOWLEDGE AND SMART TECHNOLOGY (KST 2018) - CYBERNETICS IN THE NEXT DECADES, 2018, : XVI - XVI
  • [38] CODATA and global challenges in data-driven science
    Rybkina, A.
    Hodson, S.
    Gvishiani, A.
    Kabat, P.
    Krasnoperov, R.
    Samokhina, O.
    Firsova, E.
    RUSSIAN JOURNAL OF EARTH SCIENCES, 2018, 18 (04):
  • [39] Maximizing the Science in the Era of Data-Driven Astronomy
    Aloisi, Alessandra
    ASTRONOMICAL DATA ANALYSIS SOFTWARE AND SYSTEMS XXV, 2017, 512 : 3 - 12
  • [40] Data-driven modeling and learning in science and engineering
    Montans, Francisco J.
    Chinesta, Francisco
    Gomez-Bombarelli, Rafael
    Kutz, J. Nathan
    COMPTES RENDUS MECANIQUE, 2019, 347 (11): : 845 - 855