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相关概念视频

In-situ Hybridization02:31

In-situ Hybridization

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In situ hybridization (ISH) is a technique used to detect and localize specific DNA or RNA molecules in cells, tissue, or tissue sections using a labeled probe. The technique was first used in 1969 for the investigation of nucleic acids. It is currently an essential tool in scientific research and clinical settings, especially for diagnostic purposes.
Types of probes and labels
A probe is a complementary strand of DNA or RNA that binds to corresponding nucleotide sequences in a cell. Many...
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DNA Microarrays02:34

DNA Microarrays

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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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FISH - Fluorescent In-situ Hybridization02:07

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Fluorescence in situ hybridization, or FISH, was developed in the early 1980s and has quickly become one of the most widely used techniques in cytogenetics. Labeled probes are used to bind complementary DNA or RNA sequences on a chromosome or in a region within a cell. Earlier, the probes could only be obtained by cloning or reverse transcription of a DNA template. Currently, the probe oligonucleotides can be synthesized synthetically. Additionally, with the advancement of optical techniques,...
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相关实验视频

Updated: Jun 7, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
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基因空间:基因组学启发的空间转录组预测和表征引擎.

Shivam Kumar1, Samrat Chatterjee1

  • 1Complex Analysis Group, Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad-Gurgaon Expressway, Faridabad, 121001, India.

Methods (San Diego, Calif.)
|November 9, 2024
PubMed
概括
此摘要是机器生成的。

HistoSPACE利用多种空间转录学 (ST) 数据从组织图像中提取分子见解,将基因表达与疾病病理联系起来. 这种高效的人工智能模型通过分析组织图像来帮助精准医学开发.

关键词:
表达式预测的预测图像自动编码器 图像自动编码器知识转移知识转移.空间转录组学 空间转录组学

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科学领域:

  • 计算生物学 计算生物学
  • 生物信息学是一种生物信息学.
  • 人工智能在医学中的应用

背景情况:

  • 空间转录学 (ST) 可视化了组织形态中的基因表达,为精密医学提供了潜力.
  • ST的临床翻译受到高成本和专业知识要求的阻碍.
  • 目前组织学中的人工智能与有限的信息多样性作斗争.

研究的目的:

  • 开发HistoSPACE,一种用于从各种ST组织图像中提取分子见解的AI模型.
  • 将预测的基因表达与疾病病理学联系起来.
  • 为更广泛的可访问性创建一个计算效率高的模型.

主要方法:

  • 从通用自动编码器开发了一个图像编码器,与卷积块集成.
  • 使用ST数据对模型进行了微调.
  • 采用轻量级架构,参数较少,减少记忆和训练时间.

主要成果:

  • 在leave-one-out交叉验证中实现了0.56的显著相关性.
  • 证明了效率优于当代算法.
  • 在独立数据集上验证了可靠性,显示了与疾病病理学的准确预测.

结论:

  • HistoSPACE有效地从各种ST数据中提取分子见解,将基因表达与组织病理相关联.
  • 该模型的效率和稳定性支持其对临床翻译和精准医学的潜力.
  • 这种方法提高了AI在分析复杂的生物成像数据中的实用性.