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相关概念视频

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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SPIN-AI:一种深度学习模型,可以识别空间预测基因.

Kevin Meng-Lin1, Choong-Yong Ung1, Cheng Zhang1

  • 1Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA.

Biomolecules
|June 28, 2023
PubMed
概括

我们开发了空间信息化人工智能 (SPIN-AI),以识别空间预测基因 (SPG),这些基因比空间变量基因 (SVG) 更好地捕捉细胞组织. SPIN-AI为了解组织中的空间生物学和基因功能提供了一种新的方法.

关键词:
人工智能的人工智能是人工智能.细胞利基的细胞利基空间基因调节的空间基因调节空间转录学 空间转录学

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科学领域:

  • 计算生物学 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:

  • 空间解析的测序揭示了细胞组织.
  • 空间变异基因 (SVGs) 识别具有空间变异表达的基因.
  • SVG并不能捕获所有空间组织信息.

研究的目的:

  • 开发一个深度学习模型,空间信息化人工智能 (SPIN-AI),以识别空间预测基因 (SPG).
  • 为了证明SPIN-AI使用空间转录基因数据预测细胞空间组织的能力.
  • 将SPG与SVG进行比较,以确定细胞中的生物相关性.

主要方法:

  • 设计了一个深度学习模型,命名为空间信息化人工智能 (SPIN-AI).
  • 将SPIN-AI应用于状细胞癌 (SCC) 的空间转录基因数据.
  • 分析并将已识别的SPG与先前识别的SVG进行比较.

主要成果:

  • SPIN-AI成功地确定了预测细胞空间组织的SPG.
  • 与SVG相比,SPG重新总结了SCC生物学,并确定了与SVG相比不同的基因.
  • 有相当数量的核糖体基因被确定为SPG,但不是SVG.

结论:

  • 由SPIN-AI识别的SPG捕获的生物相关信息比SVG更多,用于预测细胞组织.
  • SPIN-AI提供了一种强大的工具,用于检测SPG,并了解控制细胞组织的生物过程.
  • 这种方法在空间转录学和系统生物学研究中具有广泛的应用.