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相关概念视频

Genomics02:02

Genomics

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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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Evolutionary Relationships through Genome Comparisons02:54

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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: Jul 8, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
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解释性人工智能对omics数据:一个系统的映射研究研究.

Philipp A Toussaint1,2, Florian Leiser1, Scott Thiebes1

  • 1Department of Economics and Management, Karlsruhe Institute of Technology, Karlsruhe, Germany.

Briefings in bioinformatics
|December 19, 2023
PubMed
概括
此摘要是机器生成的。

可解释的人工智能 (XAI) 对于分析omics数据至关重要. 这项研究绘制了XAI在奥米克应用的地图,确定了趋势,研究差距以及生物见解和临床实践的未来方向.

关键词:
生物医学数据 生物医学数据可解释的人工智能可解释的人工智能机器学习是机器学习.俄米克斯 (omicsics) 是一个电子产品.系统地图研究研究系统地图研究.

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科学领域:

  • 生物信息学是一种生物信息学.
  • 人工智能的人工智能
  • 基因组学就是基因组学.

背景情况:

  • 可解释的人工智能 (XAI) 越来越多地用于omics数据分析.
  • 跨学科的性质导致了碎片化的发现,需要一个整合的概述.
  • 需要进行系统的映射研究来综合目前关于XAI的研究.

研究的目的:

  • 为了提供一个全面的概述XAI应用在omics数据分析.
  • 确定常用的AI和可解释性方法.
  • 突出研究差距,并建议未来的研究方向.

主要方法:

  • 系统地绘制科学文献的研究.
  • 搜索了Scopus,PubMed,科学网,BioRxiv,MedRxiv,以及arXiv. 这些网站的搜索结果.
  • 为人工智能方法,可解释性和omics数据开发了一个具有10个方面的编码方案.

主要成果:

  • 包括2010-2023年间发表的405篇论文.
  • 使用各种人工智能方法分析了基因组,转录组,蛋白质组和代谢组数据.
  • 确定了特征相关性和视觉解释作为常见的临时后方法,以及可解释方法的透明模型.

结论:

  • 在XAI中,对于omics数据存在显著的研究缺口.
  • 提出了八个研究方向,以推动该领域的发展.
  • 对于XAI在奥米克研究中的临床采用,仍然存在挑战.