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Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
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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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Updated: Jul 5, 2025

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
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多omics集成与加权亲和和和自我扩散用于癌症亚型识别的应用.

Xin Duan1,2, Xinnan Ding3, Zhuanzhe Zhao4,5,6

  • 1School of Artificial Intelligence, Anhui Polytechnic University, Wuhu, 241000, China. duanx@ahpu.edu.cn.

Journal of translational medicine
|January 19, 2024
PubMed
概括

我们开发了MOSD,一种高效的多omics集成方法用于癌症亚型. 这种方法改善了患者生存预测,并提供了对癌症异质性的生物学见解.

关键词:
癌症异质性的异质性这就是Muti-omics.自己扩散的自我扩散.有权重的亲和力.

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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.
  • 癌症研究 癌症研究

背景情况:

  • 精确的癌症分子亚型化对于个性化治疗和预后至关重要.
  • 多学科数据集成提供了更深入的生物学见解,但面临着计算和数据权重的挑战.
  • 现有的方法往往缺乏有效性和强大的数据类型权重,用于癌症亚型.

研究的目的:

  • 介绍MOSD (通过加权亲和力和自我扩散进行多omics集成),一种用于癌症亚型的高效计算方法.
  • 为了解决当前多omics集成工具中计算效率和数据类型权重赋值的局限性.
  • 使用多omics数据剖析癌症异质性,以改善临床应用.

主要方法:

  • 开发了MOSD,通过加权的亲和力和自我扩散整合了多omics数据 (基因表达,DNA甲基化,miRNA).
  • 构建每个数据类型的局部缩放亲和度,然后进行加权的线性组合以进行整合.
  • 应用自我扩散来增强下游集群分析的患者相似性.
  • 在十种癌症类型中验证了MOSD.

主要成果:

  • MOSD在已识别的分子亚型中显示了患者存活率的显著差异.
  • 与最先进的集成技术相比,该方法显示了计算效率.
  • 鉴定的分子亚型表现出强大的生物解释性.
  • MOSD有效地使用多omics数据剖析癌症异质性.

结论:

  • MOSD提供了一种高效和有效的方法,用于在癌症亚型化中的多omics数据集成.
  • 该方法增强了生物解释性,并改善了患者生存预测.
  • MOSD为了解癌症异质性和推进个性化瘤学提供了有价值的工具.
  • 开源代码可用于可复制性和进一步研究.