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相关概念视频

Genomics02:02

Genomics

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...
Proteomics01:33

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term proteomics...

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语义技术在多omics数据集成中的系统映射研究.

Giovanni Maria De Filippis1, Domenico Amalfitano1, Cristiano Russo1

  • 1Department of Electrical Engineering and Information Technology DIETI, University of Naples Federico II, Via Claudio, 21, Naples, 80125, Italy.

Journal of biomedical informatics
|March 28, 2025
PubMed
概括

语义技术增强了多学科数据集成,克服了异质性和可扩展性的挑战. 这种方法改善了数据分析,导致更好的基因发现和疾病洞察力.

关键词:
综合生物信息学是一个整体的生物信息学.知识图是知识图.多种经济体的整合.存在学 (Ontologies) 是一种存在学.语义技术 语义技术是指语义技术.系统地图研究研究系统地图研究.

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Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease
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科学领域:

  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学
  • 基因组学就是基因组学.

背景情况:

  • 多学科数据集成对于理解复杂的生物系统至关重要.
  • 挑战包括数据异质性,标准化和计算可扩展性.
  • 语义技术为解决这些整合问题提供了一种新的方法.

研究的目的:

  • 探索用于多omics数据集成的语义技术的应用.
  • 评估本体论,知识图表和基于图表的方法的影响.
  • 为了提高数据标准化,分析和发现在多omics研究.

主要方法:

  • 从2014年到2024年对文学进行系统地绘制研究.
  • 专注于利用语义技术用于多omics集成的研究.
  • 分析趋势和应用本体学和知识图.

主要成果:

  • 关于多omics集成的语义技术的出版物显著增加.
  • 在数据可视化,查询和管理方面取得了明显的改进.
  • 增强基因和途径的发现,更深入的疾病洞察力,以及改进的预测建模.

结论:

  • 语义技术对于克服多领域的整合挑战至关重要.
  • 未来的工作应该集中在整合各种数据类型和开发先进的计算工具.
  • 人工智能和机器学习的整合可以促进个性化医疗应用.