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相关概念视频

Longitudinal Research02:20

Longitudinal Research

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Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
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Genomics02:02

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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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相关实验视频

Updated: Sep 12, 2025

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
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在AI时代的纵向大生物数据.

Adil Mardinoglu1,2, Hasan Turkez3, Minho Shong4

  • 1Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden. adilm@scilifelab.se.

Molecular systems biology
|August 6, 2025
PubMed
概括

人工智能 (AI) 和系统生物学利用大生物数据进行健康洞察. 这种方法整合了多学科,临床和环境数据,以推进精准医学和人工智能驱动的医疗保健.

关键词:
人工智能的人工智能数字双胞胎是一个数字双胞胎.纵向多omics数据长度数据精准医学是一门精准的医学.系统生物学 系统生物学

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科学领域:

  • 计算生物学是一种计算生物学.
  • 基因组学就是基因组学.
  • 系统生物学 系统生物学

背景情况:

  • 大生物数据整合了多种omics,临床,可穿戴,成像,饮食,药物和环境因素.
  • 奥米克技术和计算能力的进步使复杂的生物数据分析成为可能.
  • 纵向和多层数据对于理解健康和疾病中的全身功能至关重要.

研究的目的:

  • 审查AI和系统生物学应用在多omics数据集成和解释.
  • 突出大生物数据在创造数字双胞胎和发现生物标志物/药物标中的作用.
  • 讨论将大生物数据纳入AI驱动医疗保健的临床实践.

主要方法:

  • 综述近期人工智能和系统生物学在多omics数据分析中的应用.
  • 检查全球多学科数据集,以了解生物层随时间的相互作用.
  • 对临床决策支持系统的大型生物数据集成的分析.

主要成果:

  • 人工智能和系统生物学有助于创建数字双胞胎,识别新生物标志物和药物标.
  • 多omics数据集揭示了跨生物层的时间相互作用,提高了精确的健康.
  • 整合大生物数据对于开发人工智能驱动的临床决策支持系统至关重要.

结论:

  • 通过人工智能和系统生物学集成的大型生物数据是精准医学的基础.
  • 人工智能驱动医院的发展需要强大的临床决策支持系统.
  • 对人工智能和基于系统生物学的医疗保健模型进行范式转变是必不可少的.